Semantic Context Enhances the Early Auditory Encoding of Natural Speech

Speaker-listener neural coupling correlates with semantic and acoustic features of naturalistic speech

Recent research has extensively reported the phenomenon of inter-brain neural coupling between speakers and listeners during speech communication. Yet, the specific speech processes underlying this neural coupling remain elusive. To bridge this gap, this study estimated the correlation between the temporal dynamics of speaker-listener neural coupling with speech features, utilizing two inter-brain datasets accounting for different noise levels and listener's language experiences (native vs. non-native). We first derived time-varying speaker-listener neural coupling, extracted acoustic feature (envelope) and semantic features (entropy and surprisal) from speech, and then explored their correlational relationship. Our findings reveal that in clear conditions, speaker-listener neural coupling correlates with semantic features. However, as noise increases, this correlation is only significant for native listeners. For non-native listeners, neural coupling correlates predominantly with acoustic feature rather than semantic features. These results revealed how speaker-listener neural coupling is associated with the acoustic and semantic features under various scenarios, enriching our understanding of the inter-brain neural mechanisms during natural speech communication. We therefore advocate for more attention on the dynamic nature of speaker-listener neural coupling and its modeling with multilevel speech features.

Neural Tracking of Speech Acoustics in Noise Is Coupled with Lexical Predictability as Estimated by Large Language Models

Adults heard recordings of two spatially separated speakers reading newspaper and magazine articles. They were asked to listen to one of them and ignore the other, and EEG was recorded to assess their neural processing. Machine learning extracted neural sources that tracked the target and distractor speakers at three levels: the acoustic envelope of speech (delta- and theta-band modulations), lexical frequency for individual words, and the contextual predictability of individual words estimated by GPT-4 and earlier lexical models. To provide a broader view of speech perception, half of the subjects completed a simultaneous visual task, and the listeners included both native and non-native English speakers. Distinct neural components were extracted for these levels of auditory and lexical processing, demonstrating that native English speakers had greater target-distractor separation compared with non-native English speakers on most measures, and that lexical processing was reduced by the visual task. Moreover, there was a novel interaction of lexical predictability and frequency with auditory processing; acoustic tracking was stronger for lexically harder words, suggesting that people listened harder to the acoustics when needed for lexical selection. This demonstrates that speech perception is not simply a feedforward process from acoustic processing to the lexicon. Rather, the adaptable context-sensitive processing long known to occur at a lexical level has broader consequences for perception, coupling with the acoustic tracking of individual speakers in noise.

Early language experience modulates the tradeoff between acoustic-temporal and lexico-semantic cortical tracking of speech

Cortical tracking of speech is relevant for the development of speech perception skills. However, no study to date has explored whether and how cortical tracking of speech is shaped by accumulated language experience, the central question of this study. In 35 bilingual children (6-year-old) with considerably bigger experience in one language, we collected electroencephalography data while they listened to continuous speech in their two languages. Cortical tracking of speech was assessed at acoustic-temporal and lexico-semantic levels. Children showed more robust acoustic-temporal tracking in the least experienced language, and more sensitive cortical tracking of semantic information in the most experienced language. Additionally, and only for the most experienced language, acoustic-temporal tracking was specifically linked to phonological abilities, and lexico-semantic tracking to vocabulary knowledge. Our results indicate that accumulated linguistic experience is a relevant maturational factor for the cortical tracking of speech at different levels during early language acquisition.

Speech prosody enhances the neural processing of syntax

Human language relies on the correct processing of syntactic information, as it is essential for successful communication between speakers. As an abstract level of language, syntax has often been studied separately from the physical form of the speech signal, thus often masking the interactions that can promote better syntactic processing in the human brain. However, behavioral and neural evidence from adults suggests the idea that prosody and syntax interact, and studies in infants support the notion that prosody assists language learning. Here we analyze a MEG dataset to investigate how acoustic cues, specifically prosody, interact with syntactic representations in the brains of native English speakers. More specifically, to examine whether prosody enhances the cortical encoding of syntactic representations, we decode syntactic phrase boundaries directly from brain activity, and evaluate possible modulations of this decoding by the prosodic boundaries. Our findings demonstrate that the presence of prosodic boundaries improves the neural representation of phrase boundaries, indicating the facilitative role of prosodic cues in processing abstract linguistic features. This work has implications for interactive models of how the brain processes different linguistic features. Future research is needed to establish the neural underpinnings of prosody-syntax interactions in languages with different typological characteristics.

Exploring neural tracking of acoustic and linguistic speech representations in individuals with post-stroke aphasia

Aphasia is a communication disorder that affects processing of language at different levels (e.g., acoustic, phonological, semantic). Recording brain activity via Electroencephalography while people listen to a continuous story allows to analyze brain responses to acoustic and linguistic properties of speech. When the neural activity aligns with these speech properties, it is referred to as neural tracking. Even though measuring neural tracking of speech may present an interesting approach to studying aphasia in an ecologically valid way, it has not yet been investigated in individuals with stroke-induced aphasia. Here, we explored processing of acoustic and linguistic speech representations in individuals with aphasia in the chronic phase after stroke and age-matched healthy controls. We found decreased neural tracking of acoustic speech representations (envelope and envelope onsets) in individuals with aphasia. In addition, word surprisal displayed decreased amplitudes in individuals with aphasia around 195 ms over frontal electrodes, although this effect was not corrected for multiple comparisons. These results show that there is potential to capture language processing impairments in individuals with aphasia by measuring neural tracking of continuous speech. However, more research is needed to validate these results. Nonetheless, this exploratory study shows that neural tracking of naturalistic, continuous speech presents a powerful approach to studying aphasia.

The contribution of semantic distance knowledge to size constancy in perception and grasping when visual cues are limited

To achieve a stable perception of object size in spite of variations in viewing distance, our visual system needs to combine retinal image information and distance cues. Previous research has shown that, not only retinal cues, but also extraretinal sensory signals can provide reliable information about depth and that different neural networks (perception versus action) can exhibit preferences in the use of these different sources of information during size-distance computations. Semantic knowledge of distance, a purely cognitive signal, can also provide distance information. Do the perception and action systems show differences in their ability to use this information in calculating object size and distance? To address this question, we presented 'glow-in-the-dark' objects of different physical sizes at different real distances in a completely dark room. Participants viewed the objects monocularly through a 1-mm pinhole. They either estimated the size and distance of the objects or attempted to grasp them. Semantic knowledge was manipulated by providing an auditory cue about the actual distance of the object: "20 cm", "30 cm", and "40 cm". We found that semantic knowledge of distance contributed to some extent to size constancy operations during perceptual estimation and grasping, but size constancy was never fully restored. Importantly, the contribution of knowledge about distance to size constancy was equivalent between perception and action. Overall, our study reveals similarities and differences between the perception and action systems in the use of semantic distance knowledge and suggests that this cognitive signal is useful but not a reliable depth cue for size constancy under restricted viewing conditions.

Resolving competing predictions in speech: How qualitatively different cues and cue reliability contribute to phoneme identification

Listeners have many sources of information available in interpreting speech. Numerous theoretical frameworks and paradigms have established that various constraints impact the processing of speech sounds, but it remains unclear how listeners might simultaneously consider multiple cues, especially those that differ qualitatively (i.e., with respect to timing and/or modality) or quantitatively (i.e., with respect to cue reliability). Here, we establish that cross-modal identity priming can influence the interpretation of ambiguous phonemes (Exp. 1, N = 40) and show that two qualitatively distinct cues - namely, cross-modal identity priming and auditory co-articulatory context - have additive effects on phoneme identification (Exp. 2, N = 40). However, we find no effect of quantitative variation in a cue - specifically, changes in the reliability of the priming cue did not influence phoneme identification (Exp. 3a, N = 40; Exp. 3b, N = 40). Overall, we find that qualitatively distinct cues can additively influence phoneme identification. While many existing theoretical frameworks address constraint integration to some degree, our results provide a step towards understanding how information that differs in both timing and modality is integrated in online speech perception.

Attention to audiovisual speech shapes neural processing through feedback-feedforward loops between different nodes of the speech network

Selective attention-related top-down modulation plays a significant role in separating relevant speech from irrelevant background speech when vocal attributes separating concurrent speakers are small and continuously evolving. Electrophysiological studies have shown that such top-down modulation enhances neural tracking of attended speech. Yet, the specific cortical regions involved remain unclear due to the limited spatial resolution of most electrophysiological techniques. To overcome such limitations, we collected both electroencephalography (EEG) (high temporal resolution) and functional magnetic resonance imaging (fMRI) (high spatial resolution), while human participants selectively attended to speakers in audiovisual scenes containing overlapping cocktail party speech. To utilise the advantages of the respective techniques, we analysed neural tracking of speech using the EEG data and performed representational dissimilarity-based EEG-fMRI fusion. We observed that attention enhanced neural tracking and modulated EEG correlates throughout the latencies studied. Further, attention-related enhancement of neural tracking fluctuated in predictable temporal profiles. We discuss how such temporal dynamics could arise from a combination of interactions between attention and prediction as well as plastic properties of the auditory cortex. EEG-fMRI fusion revealed attention-related iterative feedforward-feedback loops between hierarchically organised nodes of the ventral auditory object related processing stream. Our findings support models where attention facilitates dynamic neural changes in the auditory cortex, ultimately aiding discrimination of relevant sounds from irrelevant ones while conserving neural resources.

The organization of the semantic network as reflected by the neural correlates of six semantic dimensions

Multiple sensory-motor and non-sensory-motor dimensions have been proposed for semantic representation, but it remains unclear how the semantic system is organized along them in the human brain. Using naturalistic fMRI data and large-scale semantic ratings, we investigated the overlaps and dissociations between the neural correlates of six semantic dimensions: vision, motor, socialness, emotion, space, and time. Our findings revealed a more complex semantic atlas than what is predicted by current neurobiological models of semantic representation. Brain regions that are selectively sensitive to specific semantic dimensions were found both within and outside the brain networks assumed to represent multimodal general and/or abstract semantics. Overlaps between the neural correlates of different semantic dimensions were mainly found inside the default mode network, concentrated in the left anterior superior temporal gyrus and angular gyrus, which have been proposed as two connector hubs that bridge the multimodal experiential semantic system and the language-supported semantic system.

Identifying temporal correlations between natural single-shot videos and EEG signals

Objective.Electroencephalography (EEG) is a widely used technology for recording brain activity in brain-computer interface (BCI) research, where understanding the encoding-decoding relationship between stimuli and neural responses is a fundamental challenge. Recently, there is a growing interest in encoding-decoding natural stimuli in a single-trial setting, as opposed to traditional BCI literature where multi-trial presentations of synthetic stimuli are commonplace. While EEG responses to natural speech have been extensively studied, such stimulus-following EEG responses to natural video footage remain underexplored.Approach.We collect a new EEG dataset with subjects passively viewing a film clip and extract a few video features that have been found to be temporally correlated with EEG signals. However, our analysis reveals that these correlations are mainly driven by shot cuts in the video. To avoid the confounds related to shot cuts, we construct another EEG dataset with natural single-shot videos as stimuli and propose a new set of object-based features.Main results.We demonstrate that previous video features lack robustness in capturing the coupling with EEG signals in the absence of shot cuts, and that the proposed object-based features exhibit significantly higher correlations. Furthermore, we show that the correlations obtained with these proposed features are not dominantly driven by eye movements. Additionally, we quantitatively verify the superiority of the proposed features in a match-mismatch task. Finally, we evaluate to what extent these proposed features explain the variance in coherent stimulus responses across subjects.Significance.This work provides valuable insights into feature design for video-EEG analysis and paves the way for applications such as visual attention decoding.

Distinct roles of delta- and theta-band neural tracking for sharpening and predictive coding of multi-level speech features during spoken language processing

The brain tracks and encodes multi-level speech features during spoken language processing. It is evident that this speech tracking is dominant at low frequencies (<8 Hz) including delta and theta bands. Recent research has demonstrated distinctions between delta- and theta-band tracking but has not elucidated how they differentially encode speech across linguistic levels. Here, we hypothesised that delta-band tracking encodes prediction errors (enhanced processing of unexpected features) while theta-band tracking encodes neural sharpening (enhanced processing of expected features) when people perceive speech with different linguistic contents. EEG responses were recorded when normal-hearing participants attended to continuous auditory stimuli that contained different phonological/morphological and semantic contents: (1) real-words, (2) pseudo-words and (3) time-reversed speech. We employed multivariate temporal response functions to measure EEG reconstruction accuracies in response to acoustic (spectrogram), phonetic and phonemic features with the partialling procedure that singles out unique contributions of individual features. We found higher delta-band accuracies for pseudo-words than real-words and time-reversed speech, especially during encoding of phonetic features. Notably, individual time-lag analyses showed that significantly higher accuracies for pseudo-words than real-words started at early processing stages for phonetic encoding (<100 ms post-feature) and later stages for acoustic and phonemic encoding (>200 and 400 ms post-feature, respectively). Theta-band accuracies, on the other hand, were higher when stimuli had richer linguistic content (real-words > pseudo-words > time-reversed speech). Such effects also started at early stages (<100 ms post-feature) during encoding of all individual features or when all features were combined. We argue these results indicate that delta-band tracking may play a role in predictive coding leading to greater tracking of pseudo-words due to the presence of unexpected/unpredicted semantic information, while theta-band tracking encodes sharpened signals caused by more expected phonological/morphological and semantic contents. Early presence of these effects reflects rapid computations of sharpening and prediction errors. Moreover, by measuring changes in EEG alpha power, we did not find evidence that the observed effects can be solitarily explained by attentional demands or listening efforts. Finally, we used directed information analyses to illustrate feedforward and feedback information transfers between prediction errors and sharpening across linguistic levels, showcasing how our results fit with the hierarchical Predictive Coding framework. Together, we suggest the distinct roles of delta and theta neural tracking for sharpening and predictive coding of multi-level speech features during spoken language processing.

Intonation Units in Spontaneous Speech Evoke a Neural Response

Spontaneous speech is produced in chunks called intonation units (IUs). IUs are defined by a set of prosodic cues and presumably occur in all human languages. Recent work has shown that across different grammatical and sociocultural conditions IUs form rhythms of ∼1 unit per second. Linguistic theory suggests that IUs pace the flow of information in the discourse. As a result, IUs provide a promising and hitherto unexplored theoretical framework for studying the neural mechanisms of communication. In this article, we identify a neural response unique to the boundary defined by the IU. We measured the EEG of human participants (of either sex), who listened to different speakers recounting an emotional life event. We analyzed the speech stimuli linguistically and modeled the EEG response at word offset using a GLM approach. We find that the EEG response to IU-final words differs from the response to IU-nonfinal words even when equating acoustic boundary strength. Finally, we relate our findings to the body of research on rhythmic brain mechanisms in speech processing. We study the unique contribution of IUs and acoustic boundary strength in predicting delta-band EEG. This analysis suggests that IU-related neural activity, which is tightly linked to the classic Closure Positive Shift (CPS), could be a time-locked component that captures the previously characterized delta-band neural speech tracking.SIGNIFICANCE STATEMENT Linguistic communication is central to human experience, and its neural underpinnings are a topic of much research in recent years. Neuroscientific research has benefited from studying human behavior in naturalistic settings, an endeavor that requires explicit models of complex behavior. Usage-based linguistic theory suggests that spoken language is prosodically structured in intonation units. We reveal that the neural system is attuned to intonation units by explicitly modeling their impact on the EEG response beyond mere acoustics. To our understanding, this is the first time this is demonstrated in spontaneous speech under naturalistic conditions and under a theoretical framework that connects the prosodic chunking of speech, on the one hand, with the flow of information during communication, on the other.

Leading and following: Noise differently affects semantic and acoustic processing during naturalistic speech comprehension

Despite the distortion of speech signals caused by unavoidable noise in daily life, our ability to comprehend speech in noisy environments is relatively stable. However, the neural mechanisms underlying reliable speech-in-noise comprehension remain to be elucidated. The present study investigated the neural tracking of acoustic and semantic speech information during noisy naturalistic speech comprehension. Participants listened to narrative audio recordings mixed with spectrally matched stationary noise at three signal-to-ratio (SNR) levels (no noise, 3 dB, -3 dB), and 60-channel electroencephalography (EEG) signals were recorded. A temporal response function (TRF) method was employed to derive event-related-like responses to the continuous speech stream at both the acoustic and the semantic levels. Whereas the amplitude envelope of the naturalistic speech was taken as the acoustic feature, word entropy and word surprisal were extracted via the natural language processing method as two semantic features. Theta-band frontocentral TRF responses to the acoustic feature were observed at around 400 ms following speech fluctuation onset over all three SNR levels, and the response latencies were more delayed with increasing noise. Delta-band frontal TRF responses to the semantic feature of word entropy were observed at around 200 to 600 ms leading to speech fluctuation onset over all three SNR levels. The response latencies became more leading with increasing noise and decreasing speech comprehension and intelligibility. While the following responses to speech acoustics were consistent with previous studies, our study revealed the robustness of leading responses to speech semantics, which suggests a possible predictive mechanism at the semantic level for maintaining reliable speech comprehension in noisy environments.

EEG-based speaker-listener neural coupling reflects speech-selective attentional mechanisms beyond the speech stimulus

When we pay attention to someone, do we focus only on the sound they make, the word they use, or do we form a mental space shared with the speaker we want to pay attention to? Some would argue that the human language is no other than a simple signal, but others claim that human beings understand each other because they form a shared mental ground between the speaker and the listener. Our study aimed to explore the neural mechanisms of speech-selective attention by investigating the electroencephalogram-based neural coupling between the speaker and the listener in a cocktail party paradigm. The temporal response function method was employed to reveal how the listener was coupled to the speaker at the neural level. The results showed that the neural coupling between the listener and the attended speaker peaked 5 s before speech onset at the delta band over the left frontal region, and was correlated with speech comprehension performance. In contrast, the attentional processing of speech acoustics and semantics occurred primarily at a later stage after speech onset and was not significantly correlated with comprehension performance. These findings suggest a predictive mechanism to achieve speaker-listener neural coupling for successful speech comprehension.

Prediction during language comprehension: what is next?

Prediction is often regarded as an integral aspect of incremental language comprehension, but little is known about the cognitive architectures and mechanisms that support it. We review studies showing that listeners and readers use all manner of contextual information to generate multifaceted predictions about upcoming input. The nature of these predictions may vary between individuals owing to differences in language experience, among other factors. We then turn to unresolved questions which may guide the search for the underlying mechanisms. (i) Is prediction essential to language processing or an optional strategy? (ii) Are predictions generated from within the language system or by domain-general processes? (iii) What is the relationship between prediction and memory? (iv) Does prediction in comprehension require simulation via the production system? We discuss promising directions for making progress in answering these questions and for developing a mechanistic understanding of prediction in language.

An interpretable measure of semantic similarity for predicting eye movements in reading

Predictions about upcoming content play an important role during language comprehension and processing. Semantic similarity as a metric has been used to predict how words are processed in context in language comprehension and processing tasks. This study proposes a novel, dynamic approach for computing contextual semantic similarity, evaluates the extent to which the semantic similarity measures computed using this approach can predict fixation durations in reading tasks recorded in a corpus of eye-tracking data, and compares the performance of these measures to that of semantic similarity measures computed using the cosine and Euclidean methods. Our results reveal that the semantic similarity measures generated by our approach are significantly predictive of fixation durations on reading and outperform those generated by the two existing approaches. The findings of this study contribute to a better understanding of how humans process words in context and make predictions in language comprehension and processing. The effective and interpretable approach to computing contextual semantic similarity proposed in this study can also facilitate further explorations of other experimental data on language comprehension and processing.

The effects of speech masking on neural tracking of acoustic and semantic features of natural speech

Listening environments contain background sounds that mask speech and lead to communication challenges. Sensitivity to slow acoustic fluctuations in speech can help segregate speech from background noise. Semantic context can also facilitate speech perception in noise, for example, by enabling prediction of upcoming words. However, not much is known about how different degrees of background masking affect the neural processing of acoustic and semantic features during naturalistic speech listening. In the current electroencephalography (EEG) study, participants listened to engaging, spoken stories masked at different levels of multi-talker babble to investigate how neural activity in response to acoustic and semantic features changes with acoustic challenges, and how such effects relate to speech intelligibility. The pattern of neural response amplitudes associated with both acoustic and semantic speech features across masking levels was U-shaped, such that amplitudes were largest for moderate masking levels. This U-shape may be due to increased attentional focus when speech comprehension is challenging, but manageable. The latency of the neural responses increased linearly with increasing background masking, and neural latency change associated with acoustic processing most closely mirrored the changes in speech intelligibility. Finally, tracking responses related to semantic dissimilarity remained robust until severe speech masking (-3 dB SNR). The current study reveals that neural responses to acoustic features are highly sensitive to background masking and decreasing speech intelligibility, whereas neural responses to semantic features are relatively robust, suggesting that individuals track the meaning of the story well even in moderate background sound.

The effect of topic familiarity and volatility of auditory scene on selective auditory attention

Selective auditory attention has been shown to modulate the cortical representation of speech. This effect has been well documented in acoustically more challenging environments. However, the influence of top-down factors, in particular topic familiarity, on this process remains unclear, despite evidence that semantic information can promote speech-in-noise perception. Apart from individual features forming a static listening condition, dynamic and irregular changes of auditory scenes-volatile listening environments-have been less studied. To address these gaps, we explored the influence of topic familiarity and volatile listening on the selective auditory attention process during dichotic listening using electroencephalography. When stories with unfamiliar topics were presented, participants' comprehension was severely degraded. However, their cortical activity selectively tracked the speech of the target story well. This implies that topic familiarity hardly influences the speech tracking neural index, possibly when the bottom-up information is sufficient. However, when the listening environment was volatile and the listeners had to re-engage in new speech whenever auditory scenes altered, the neural correlates of the attended speech were degraded. In particular, the cortical response to the attended speech and the spatial asymmetry of the response to the left and right attention were significantly attenuated around 100-200 ms after the speech onset. These findings suggest that volatile listening environments could adversely affect the modulation effect of selective attention, possibly by hampering proper attention due to increased perceptual load.

A deep hierarchy of predictions enables online meaning extraction in a computational model of human speech comprehension

Understanding speech requires mapping fleeting and often ambiguous soundwaves to meaning. While humans are known to exploit their capacity to contextualize to facilitate this process, how internal knowledge is deployed online remains an open question. Here, we present a model that extracts multiple levels of information from continuous speech online. The model applies linguistic and nonlinguistic knowledge to speech processing, by periodically generating top-down predictions and incorporating bottom-up incoming evidence in a nested temporal hierarchy. We show that a nonlinguistic context level provides semantic predictions informed by sensory inputs, which are crucial for disambiguating among multiple meanings of the same word. The explicit knowledge hierarchy of the model enables a more holistic account of the neurophysiological responses to speech compared to using lexical predictions generated by a neural network language model (GPT-2). We also show that hierarchical predictions reduce peripheral processing via minimizing uncertainty and prediction error. With this proof-of-concept model, we demonstrate that the deployment of hierarchical predictions is a possible strategy for the brain to dynamically utilize structured knowledge and make sense of the speech input.

Neural tracking of linguistic and acoustic speech representations decreases with advancing age

BACKGROUND

Older adults process speech differently, but it is not yet clear how aging affects different levels of processing natural, continuous speech, both in terms of bottom-up acoustic analysis and top-down generation of linguistic-based predictions. We studied natural speech processing across the adult lifespan via electroencephalography (EEG) measurements of neural tracking.

GOALS

Our goals are to analyze the unique contribution of linguistic speech processing across the adult lifespan using natural speech, while controlling for the influence of acoustic processing. Moreover, we also studied acoustic processing across age. In particular, we focus on changes in spatial and temporal activation patterns in response to natural speech across the lifespan.

METHODS

52 normal-hearing adults between 17 and 82 years of age listened to a naturally spoken story while the EEG signal was recorded. We investigated the effect of age on acoustic and linguistic processing of speech. Because age correlated with hearing capacity and measures of cognition, we investigated whether the observed age effect is mediated by these factors. Furthermore, we investigated whether there is an effect of age on hemisphere lateralization and on spatiotemporal patterns of the neural responses.

RESULTS

Our EEG results showed that linguistic speech processing declines with advancing age. Moreover, as age increased, the neural response latency to certain aspects of linguistic speech processing increased. Also acoustic neural tracking (NT) decreased with increasing age, which is at odds with the literature. In contrast to linguistic processing, older subjects showed shorter latencies for early acoustic responses to speech. No evidence was found for hemispheric lateralization in neither younger nor older adults during linguistic speech processing. Most of the observed aging effects on acoustic and linguistic processing were not explained by age-related decline in hearing capacity or cognition. However, our results suggest that the effect of decreasing linguistic neural tracking with advancing age at word-level is also partially due to an age-related decline in cognition than a robust effect of age.

CONCLUSION

Spatial and temporal characteristics of the neural responses to continuous speech change across the adult lifespan for both acoustic and linguistic speech processing. These changes may be traces of structural and/or functional change that occurs with advancing age.

Dataset size considerations for robust acoustic and phonetic speech encoding models in EEG

In many experiments that investigate auditory and speech processing in the brain using electroencephalography (EEG), the experimental paradigm is often lengthy and tedious. Typically, the experimenter errs on the side of including more data, more trials, and therefore conducting a longer task to ensure that the data are robust and effects are measurable. Recent studies used naturalistic stimuli to investigate the brain's response to individual or a combination of multiple speech features using system identification techniques, such as multivariate temporal receptive field (mTRF) analyses. The neural data collected from such experiments must be divided into a training set and a test set to fit and validate the mTRF weights. While a good strategy is clearly to collect as much data as is feasible, it is unclear how much data are needed to achieve stable results. Furthermore, it is unclear whether the specific stimulus used for mTRF fitting and the choice of feature representation affects how much data would be required for robust and generalizable results. Here, we used previously collected EEG data from our lab using sentence stimuli and movie stimuli as well as EEG data from an open-source dataset using audiobook stimuli to better understand how much data needs to be collected for naturalistic speech experiments measuring acoustic and phonetic tuning. We found that the EEG receptive field structure tested here stabilizes after collecting a training dataset of approximately 200 s of TIMIT sentences, around 600 s of movie trailers training set data, and approximately 460 s of audiobook training set data. Thus, we provide suggestions on the minimum amount of data that would be necessary for fitting mTRFs from naturalistic listening data. Our findings are motivated by highly practical concerns when working with children, patient populations, or others who may not tolerate long study sessions. These findings will aid future researchers who wish to study naturalistic speech processing in healthy and clinical populations while minimizing participant fatigue and retaining signal quality.

Cortical speech tracking is related to individual prediction tendencies

Listening can be conceptualized as a process of active inference, in which the brain forms internal models to integrate auditory information in a complex interaction of bottom-up and top-down processes. We propose that individuals vary in their "prediction tendency" and that this variation contributes to experiential differences in everyday listening situations and shapes the cortical processing of acoustic input such as speech. Here, we presented tone sequences of varying entropy level, to independently quantify auditory prediction tendency (as the tendency to anticipate low-level acoustic features) for each individual. This measure was then used to predict cortical speech tracking in a multi speaker listening task, where participants listened to audiobooks narrated by a target speaker in isolation or interfered by 1 or 2 distractors. Furthermore, semantic violations were introduced into the story, to also examine effects of word surprisal during speech processing. Our results show that cortical speech tracking is related to prediction tendency. In addition, we find interactions between prediction tendency and background noise as well as word surprisal in disparate brain regions. Our findings suggest that individual prediction tendencies are generalizable across different listening situations and may serve as a valuable element to explain interindividual differences in natural listening situations.

More than words: Neurophysiological correlates of semantic dissimilarity depend on comprehension of the speech narrative

Speech comprehension relies on the ability to understand words within a coherent context. Recent studies have attempted to obtain electrophysiological indices of this process by modelling how brain activity is affected by a word's semantic dissimilarity to preceding words. Although the resulting indices appear robust and are strongly modulated by attention, it remains possible that, rather than capturing the contextual understanding of words, they may actually reflect word-to-word changes in semantic content without the need for a narrative-level understanding on the part of the listener. To test this, we recorded electroencephalography from subjects who listened to speech presented in either its original, narrative form, or after scrambling the word order by varying amounts. This manipulation affected the ability of subjects to comprehend the speech narrative but not the ability to recognise individual words. Neural indices of semantic understanding and low-level acoustic processing were derived for each scrambling condition using the temporal response function. Signatures of semantic processing were observed when speech was unscrambled or minimally scrambled and subjects understood the speech. The same markers were absent for higher scrambling levels as speech comprehension dropped. In contrast, word recognition remained high and neural measures related to envelope tracking did not vary significantly across scrambling conditions. This supports the previous claim that electrophysiological indices based on the semantic dissimilarity of words to their context reflect a listener's understanding of those words relative to that context. It also highlights the relative insensitivity of neural measures of low-level speech processing to speech comprehension.

Neural synchronization is strongest to the spectral flux of slow music and depends on familiarity and beat salience

Neural activity in the auditory system synchronizes to sound rhythms, and brain-environment synchronization is thought to be fundamental to successful auditory perception. Sound rhythms are often operationalized in terms of the sound's amplitude envelope. We hypothesized that - especially for music - the envelope might not best capture the complex spectro-temporal fluctuations that give rise to beat perception and synchronized neural activity. This study investigated (1) neural synchronization to different musical features, (2) tempo-dependence of neural synchronization, and (3) dependence of synchronization on familiarity, enjoyment, and ease of beat perception. In this electroencephalography study, 37 human participants listened to tempo-modulated music (1-4 Hz). Independent of whether the analysis approach was based on temporal response functions (TRFs) or reliable components analysis (RCA), the spectral flux of music - as opposed to the amplitude envelope - evoked strongest neural synchronization. Moreover, music with slower beat rates, high familiarity, and easy-to-perceive beats elicited the strongest neural response. Our results demonstrate the importance of spectro-temporal fluctuations in music for driving neural synchronization, and highlight its sensitivity to musical tempo, familiarity, and beat salience.

Neurophysiological evidence for goal-oriented modulation of speech perception

Speech perception depends on the dynamic interplay of bottom-up and top-down information along a hierarchically organized cortical network. Here, we test, for the first time in the human brain, whether neural processing of attended speech is dynamically modulated by task demand using a context-free discrimination paradigm. Electroencephalographic signals were recorded during 3 parallel experiments that differed only in the phonological feature of discrimination (word, vowel, and lexical tone, respectively). The event-related potentials (ERPs) revealed the task modulation of speech processing at approximately 200 ms (P2) after stimulus onset, probably influencing what phonological information to retain in memory. For the phonological comparison of sequential words, task modulation occurred later at approximately 300 ms (N3 and P3), reflecting the engagement of task-specific cognitive processes. The ERP results were consistent with the changes in delta-theta neural oscillations, suggesting the involvement of cortical tracking of speech envelopes. The study thus provides neurophysiological evidence for goal-oriented modulation of attended speech and calls for speech perception models incorporating limited memory capacity and goal-oriented optimization mechanisms.

A hierarchy of linguistic predictions during natural language comprehension

Understanding spoken language requires transforming ambiguous acoustic streams into a hierarchy of representations, from phonemes to meaning. It has been suggested that the brain uses prediction to guide the interpretation of incoming input. However, the role of prediction in language processing remains disputed, with disagreement about both the ubiquity and representational nature of predictions. Here, we address both issues by analyzing brain recordings of participants listening to audiobooks, and using a deep neural network (GPT-2) to precisely quantify contextual predictions. First, we establish that brain responses to words are modulated by ubiquitous predictions. Next, we disentangle model-based predictions into distinct dimensions, revealing dissociable neural signatures of predictions about syntactic category (parts of speech), phonemes, and semantics. Finally, we show that high-level (word) predictions inform low-level (phoneme) predictions, supporting hierarchical predictive processing. Together, these results underscore the ubiquity of prediction in language processing, showing that the brain spontaneously predicts upcoming language at multiple levels of abstraction.

A hierarchy of linguistic predictions during natural language comprehension

Understanding spoken language requires transforming ambiguous acoustic streams into a hierarchy of representations, from phonemes to meaning. It has been suggested that the brain uses prediction to guide the interpretation of incoming input. However, the role of prediction in language processing remains disputed, with disagreement about both the ubiquity and representational nature of predictions. Here, we address both issues by analyzing brain recordings of participants listening to audiobooks, and using a deep neural network (GPT-2) to precisely quantify contextual predictions. First, we establish that brain responses to words are modulated by ubiquitous predictions. Next, we disentangle model-based predictions into distinct dimensions, revealing dissociable neural signatures of predictions about syntactic category (parts of speech), phonemes, and semantics. Finally, we show that high-level (word) predictions inform low-level (phoneme) predictions, supporting hierarchical predictive processing. Together, these results underscore the ubiquity of prediction in language processing, showing that the brain spontaneously predicts upcoming language at multiple levels of abstraction.

The neural response at the fundamental frequency of speech is modulated by word-level acoustic and linguistic information

Spoken language comprehension requires rapid and continuous integration of information, from lower-level acoustic to higher-level linguistic features. Much of this processing occurs in the cerebral cortex. Its neural activity exhibits, for instance, correlates of predictive processing, emerging at delays of a few 100 ms. However, the auditory pathways are also characterized by extensive feedback loops from higher-level cortical areas to lower-level ones as well as to subcortical structures. Early neural activity can therefore be influenced by higher-level cognitive processes, but it remains unclear whether such feedback contributes to linguistic processing. Here, we investigated early speech-evoked neural activity that emerges at the fundamental frequency. We analyzed EEG recordings obtained when subjects listened to a story read by a single speaker. We identified a response tracking the speaker's fundamental frequency that occurred at a delay of 11 ms, while another response elicited by the high-frequency modulation of the envelope of higher harmonics exhibited a larger magnitude and longer latency of about 18 ms with an additional significant component at around 40 ms. Notably, while the earlier components of the response likely originate from the subcortical structures, the latter presumably involves contributions from cortical regions. Subsequently, we determined the magnitude of these early neural responses for each individual word in the story. We then quantified the context-independent frequency of each word and used a language model to compute context-dependent word surprisal and precision. The word surprisal represented how predictable a word is, given the previous context, and the word precision reflected the confidence about predicting the next word from the past context. We found that the word-level neural responses at the fundamental frequency were predominantly influenced by the acoustic features: the average fundamental frequency and its variability. Amongst the linguistic features, only context-independent word frequency showed a weak but significant modulation of the neural response to the high-frequency envelope modulation. Our results show that the early neural response at the fundamental frequency is already influenced by acoustic as well as linguistic information, suggesting top-down modulation of this neural response.

Timing of brain entrainment to the speech envelope during speaking, listening and self-listening

⁠This study investigates the dynamics of speech envelope tracking during speech production, listening and self-listening. We use a paradigm in which participants listen to natural speech (Listening), produce natural speech (Speech Production), and listen to the playback of their own speech (Self-Listening), all while their neural activity is recorded with EEG. After time-locking EEG data collection and auditory recording and playback, we used a Gaussian copula mutual information measure to estimate the relationship between information content in the EEG and auditory signals. In the 2-10 Hz frequency range, we identified different latencies for maximal speech envelope tracking during speech production and speech perception. Maximal speech tracking takes place approximately 110 ms after auditory presentation during perception and 25 ms before vocalisation during speech production. These results describe a specific timeline for speech tracking in speakers and listeners in line with the idea of a speech chain and hence, delays in communication.

Effects of Structure and Meaning on Cortical Tracking of Linguistic Units in Naturalistic Speech

Recent research has established that cortical activity "tracks" the presentation rate of syntactic phrases in continuous speech, even though phrases are abstract units that do not have direct correlates in the acoustic signal. We investigated whether cortical tracking of phrase structures is modulated by the extent to which these structures compositionally determine meaning. To this end, we recorded electroencephalography (EEG) of 38 native speakers who listened to naturally spoken Dutch stimuli in different conditions, which parametrically modulated the degree to which syntactic structure and lexical semantics determine sentence meaning. Tracking was quantified through mutual information between the EEG data and either the speech envelopes or abstract annotations of syntax, all of which were filtered in the frequency band corresponding to the presentation rate of phrases (1.1-2.1 Hz). Overall, these mutual information analyses showed stronger tracking of phrases in regular sentences than in stimuli whose lexical-syntactic content is reduced, but no consistent differences in tracking between sentences and stimuli that contain a combination of syntactic structure and lexical content. While there were no effects of compositional meaning on the degree of phrase-structure tracking, analyses of event-related potentials elicited by sentence-final words did reveal meaning-induced differences between conditions. Our findings suggest that cortical tracking of structure in sentences indexes the internal generation of this structure, a process that is modulated by the properties of its input, but not by the compositional interpretation of its output.

Multivariate analysis of speech envelope tracking reveals coupling beyond auditory cortex

The systematic alignment of low-frequency brain oscillations with the acoustic speech envelope signal is well established and has been proposed to be crucial for actively perceiving speech. Previous studies investigating speech-brain coupling in source space are restricted to univariate pairwise approaches between brain and speech signals, and therefore speech tracking information in frequency-specific communication channels might be lacking. To address this, we propose a novel multivariate framework for estimating speech-brain coupling where neural variability from source-derived activity is taken into account along with the rate of envelope's amplitude change (derivative). We applied it in magnetoencephalographic (MEG) recordings while human participants (male and female) listened to one hour of continuous naturalistic speech, showing that a multivariate approach outperforms the corresponding univariate method in low- and high frequencies across frontal, motor, and temporal areas. Systematic comparisons revealed that the gain in low frequencies (0.6 - 0.8 Hz) was related to the envelope's rate of change whereas in higher frequencies (from 0.8 to 10 Hz) it was mostly related to the increased neural variability from source-derived cortical areas. Furthermore, following a non-negative matrix factorization approach we found distinct speech-brain components across time and cortical space related to speech processing. We confirm that speech envelope tracking operates mainly in two timescales (δ and θ frequency bands) and we extend those findings showing shorter coupling delays in auditory-related components and longer delays in higher-association frontal and motor components, indicating temporal differences of speech tracking and providing implications for hierarchical stimulus-driven speech processing.

Ensemble size perception: Its neural signature and the role of global interaction over individual items

To efficiently process complex visual scenes, the visual system often summarizes statistical information across individual items and represents them as an ensemble. However, due to the lack of techniques to disentangle the representation of the ensemble from that of the individual items constituting the ensemble, whether there exists a specialized neural mechanism for ensemble processing and how ensemble perception is computed in the brain remain unknown. To address these issues, we used a frequency-tagging EEG approach to track brain responses to periodically updated ensemble sizes. Neural responses tracking the ensemble size were detected in parieto-occipital electrodes, revealing a global and specialized neural mechanism of ensemble size perception. We then used the temporal response function to isolate neural responses to the individual sizes and their interactions. Notably, while the individual sizes and their local and global interactions were encoded in the EEG signals, only the global interaction contributed directly to the ensemble size perception. Finally, distributed attention to the global stimulus pattern enhanced the neural signature of the ensemble size, mainly by modulating the neural representation of the global interaction between all individual sizes. These findings advocate a specialized, global neural mechanism of ensemble size perception and suggest that global interaction between individual items contributes to ensemble perception.

Age-related deficits in dip-listening evident for isolated sentences but not for spoken stories

Fluctuating background sounds facilitate speech intelligibility by providing speech ‘glimpses’ (masking release). Older adults benefit less from glimpses, but masking release is typically investigated using isolated sentences. Recent work indicates that using engaging, continuous speech materials (e.g., spoken stories) may qualitatively alter speech-in-noise listening. Moreover, neural sensitivity to different amplitude envelope profiles (ramped, damped) changes with age, but whether this affects speech listening is unknown. In three online experiments, we investigate how masking release in younger and older adults differs for masked sentences and stories, and how speech intelligibility varies with masker amplitude profile. Intelligibility was generally greater for damped than ramped maskers. Masking release was reduced in older relative to younger adults for disconnected sentences, and stories with a randomized sentence order. Critically, when listening to stories with an engaging and coherent narrative, older adults demonstrated equal or greater masking release compared to younger adults. Older adults thus appear to benefit from ‘glimpses’ as much as, or more than, younger adults when the speech they are listening to follows a coherent topical thread. Our results highlight the importance of cognitive and motivational factors for speech understanding, and suggest that previous work may have underestimated speech-listening abilities in older adults.

Enhancement of speech-in-noise comprehension through vibrotactile stimulation at the syllabic rate

Syllables are important building blocks of speech. They occur at a rate between 4 and 8 Hz, corresponding to the theta frequency range of neural activity in the cerebral cortex. When listening to speech, the theta activity becomes aligned to the syllabic rhythm, presumably aiding in parsing a speech signal into distinct syllables. However, this neural activity cannot only be influenced by sound, but also by somatosensory information. Here, we show that the presentation of vibrotactile signals at the syllabic rate can enhance the comprehension of speech in background noise. We further provide evidence that this multisensory enhancement of speech comprehension reflects the multisensory integration of auditory and tactile information in the auditory cortex.

Speech unfolds over distinct temporal scales, in particular, those related to the rhythm of phonemes, syllables, and words. When a person listens to continuous speech, the syllabic rhythm is tracked by neural activity in the theta frequency range. The tracking plays a functional role in speech processing: Influencing the theta activity through transcranial current stimulation, for instance, can impact speech perception. The theta-band activity in the auditory cortex can also be modulated through the somatosensory system, but the effect on speech processing has remained unclear. Here, we show that vibrotactile feedback presented at the rate of syllables can modulate and, in fact, enhance the comprehension of a speech signal in background noise. The enhancement occurs when vibrotactile pulses occur at the perceptual center of the syllables, whereas a temporal delay between the vibrotactile signals and the speech stream can lead to a lower level of speech comprehension. We further investigate the neural mechanisms underlying the audiotactile integration through electroencephalographic (EEG) recordings. We find that the audiotactile stimulation modulates the neural response to the speech rhythm, as well as the neural response to the vibrotactile pulses. The modulations of these neural activities reflect the behavioral effects on speech comprehension. Moreover, we demonstrate that speech comprehension can be predicted by particular aspects of the neural responses. Our results evidence a role of vibrotactile information for speech processing and may have applications in future auditory prosthesis.

Neural Activity during Story Listening Is Synchronized across Individuals Despite Acoustic Masking

Older people with hearing problems often experience difficulties understanding speech in the presence of background sound. As a result, they may disengage in social situations, which has been associated with negative psychosocial health outcomes. Measuring listening (dis)engagement during challenging listening situations has received little attention thus far. We recruit young, normal-hearing human adults (both sexes) and investigate how speech intelligibility and engagement during naturalistic story listening is affected by the level of acoustic masking (12-talker babble) at different signal-to-noise ratios (SNRs). In Experiment 1, we observed that word-report scores were above 80% for all but the lowest SNR (-3 dB SNR) we tested, at which performance dropped to 54%. In Experiment 2, we calculated intersubject correlation (ISC) using EEG data to identify dynamic spatial patterns of shared neural activity evoked by the stories. ISC has been used as a neural measure of participants' engagement with naturalistic materials. Our results show that ISC was stable across all but the lowest SNRs, despite reduced speech intelligibility. Comparing ISC and intelligibility demonstrated that word-report performance declined more strongly with decreasing SNR compared to ISC. Our measure of neural engagement suggests that individuals remain engaged in story listening despite missing words because of background noise. Our work provides a potentially fruitful approach to investigate listener engagement with naturalistic, spoken stories that may be used to investigate (dis)engagement in older adults with hearing impairment.

Generalizable EEG Encoding Models with Naturalistic Audiovisual Stimuli

In natural conversations, listeners must attend to what others are saying while ignoring extraneous background sounds. Recent studies have used encoding models to predict electroencephalography (EEG) responses to speech in noise-free listening situations, sometimes referred to as "speech tracking." Researchers have analyzed how speech tracking changes with different types of background noise. It is unclear, however, whether neural responses from acoustically rich, naturalistic environments with and without background noise can be generalized to more controlled stimuli. If encoding models for acoustically rich, naturalistic stimuli are generalizable to other tasks, this could aid in data collection from populations of individuals who may not tolerate listening to more controlled and less engaging stimuli for long periods of time. We recorded noninvasive scalp EEG while 17 human participants (8 male/9 female) listened to speech without noise and audiovisual speech stimuli containing overlapping speakers and background sounds. We fit multivariate temporal receptive field encoding models to predict EEG responses to pitch, the acoustic envelope, phonological features, and visual cues in both stimulus conditions. Our results suggested that neural responses to naturalistic stimuli were generalizable to more controlled datasets. EEG responses to speech in isolation were predicted accurately using phonological features alone, while responses to speech in a rich acoustic background were more accurate when including both phonological and acoustic features. Our findings suggest that naturalistic audiovisual stimuli can be used to measure receptive fields that are comparable and generalizable to more controlled audio-only stimuli.SIGNIFICANCE STATEMENT Understanding spoken language in natural environments requires listeners to parse acoustic and linguistic information in the presence of other distracting stimuli. However, most studies of auditory processing rely on highly controlled stimuli with no background noise, or with background noise inserted at specific times. Here, we compare models where EEG data are predicted based on a combination of acoustic, phonetic, and visual features in highly disparate stimuli-sentences from a speech corpus and speech embedded within movie trailers. We show that modeling neural responses to highly noisy, audiovisual movies can uncover tuning for acoustic and phonetic information that generalizes to simpler stimuli typically used in sensory neuroscience experiments.

Moment-by-moment tracking of naturalistic learning and its underlying hippocampo-cortical interactions

Humans form lasting memories of stimuli that were only encountered once. This naturally occurs when listening to a story, however it remains unclear how and when memories are stored and retrieved during story-listening. Here, we first confirm in behavioral experiments that participants can learn about the structure of a story after a single exposure and are able to recall upcoming words when the story is presented again. We then track mnemonic information in high frequency activity (70–200 Hz) as patients undergoing electrocorticographic recordings listen twice to the same story. We demonstrate predictive recall of upcoming information through neural responses in auditory processing regions. This neural measure correlates with behavioral measures of event segmentation and learning. Event boundaries are linked to information flow from cortex to hippocampus. When listening for a second time, information flow from hippocampus to cortex precedes moments of predictive recall. These results provide insight on a fine-grained temporal scale into how episodic memory encoding and retrieval work under naturalistic conditions.

When listening to a story, humans learn about its structure and content. Here the authors reveal the neural processes behind episodic memory and predictive recall at a fine temporal scale in this naturalistic setting

Envelope reconstruction of speech and music highlights stronger tracking of speech at low frequencies

The human brain tracks amplitude fluctuations of both speech and music, which reflects acoustic processing in addition to the encoding of higher-order features and one's cognitive state. Comparing neural tracking of speech and music envelopes can elucidate stimulus-general mechanisms, but direct comparisons are confounded by differences in their envelope spectra. Here, we use a novel method of frequency-constrained reconstruction of stimulus envelopes using EEG recorded during passive listening. We expected to see music reconstruction match speech in a narrow range of frequencies, but instead we found that speech was reconstructed better than music for all frequencies we examined. Additionally, models trained on all stimulus types performed as well or better than the stimulus-specific models at higher modulation frequencies, suggesting a common neural mechanism for tracking speech and music. However, speech envelope tracking at low frequencies, below 1 Hz, was associated with increased weighting over parietal channels, which was not present for the other stimuli. Our results highlight the importance of low-frequency speech tracking and suggest an origin from speech-specific processing in the brain.

Preparatory delta phase response is correlated with naturalistic speech comprehension performance

While human speech comprehension is thought to be an active process that involves top-down predictions, it remains unclear how predictive information is used to prepare for the processing of upcoming speech information. We aimed to identify the neural signatures of the preparatory processing of upcoming speech. Participants selectively attended to one of two competing naturalistic, narrative speech streams, and a temporal response function (TRF) method was applied to derive event-related-like neural responses from electroencephalographic data. The phase responses to the attended speech at the delta band (1-4 Hz) were correlated with the comprehension performance of individual participants, with a latency of - 200-0 ms relative to the onset of speech amplitude envelope fluctuations over the fronto-central and left-lateralized parietal electrodes. The phase responses to the attended speech at the alpha band also correlated with comprehension performance but with a latency of 650-980 ms post-onset over the fronto-central electrodes. Distinct neural signatures were found for the attentional modulation, taking the form of TRF-based amplitude responses at a latency of 240-320 ms post-onset over the left-lateralized fronto-central and occipital electrodes. Our findings reveal how the brain gets prepared to process an upcoming speech in a continuous, naturalistic speech context.

Extracting the Auditory Attention in a Dual-Speaker Scenario From EEG Using a Joint CNN-LSTM Model

Human brain performs remarkably well in segregating a particular speaker from interfering ones in a multispeaker scenario. We can quantitatively evaluate the segregation capability by modeling a relationship between the speech signals present in an auditory scene, and the listener's cortical signals measured using electroencephalography (EEG). This has opened up avenues to integrate neuro-feedback into hearing aids where the device can infer user's attention and enhance the attended speaker. Commonly used algorithms to infer the auditory attention are based on linear systems theory where cues such as speech envelopes are mapped on to the EEG signals. Here, we present a joint convolutional neural network (CNN)—long short-term memory (LSTM) model to infer the auditory attention. Our joint CNN-LSTM model takes the EEG signals and the spectrogram of the multiple speakers as inputs and classifies the attention to one of the speakers. We evaluated the reliability of our network using three different datasets comprising of 61 subjects, where each subject undertook a dual-speaker experiment. The three datasets analyzed corresponded to speech stimuli presented in three different languages namely German, Danish, and Dutch. Using the proposed joint CNN-LSTM model, we obtained a median decoding accuracy of 77.2% at a trial duration of 3 s. Furthermore, we evaluated the amount of sparsity that the model can tolerate by means of magnitude pruning and found a tolerance of up to 50% sparsity without substantial loss of decoding accuracy.

Brain electrical dynamics in speech segmentation depends upon prior experience with the language

It remains unclear whether the process of speech tracking, which facilitates speech segmentation, reflects top-down mechanisms related to prior linguistic models or stimulus-driven mechanisms, or possibly both. To address this, we recorded electroencephalography (EEG) responses from native and non-native speakers of English that had different prior experience with the English language but heard acoustically identical stimuli. Despite a significant difference in the ability to segment and perceive speech, our EEG results showed that theta-band tracking of the speech envelope did not depend significantly on prior experience with language. However, tracking in the theta-band did show changes across repetitions of the same sentence, suggesting a priming effect. Furthermore, native and non-native speakers showed different phase dynamics at word boundaries, suggesting differences in segmentation mechanisms. Finally, we found that the correlation between higher frequency dynamics reflecting phoneme-level processing and perceptual segmentation of words might depend on prior experience with the spoken language.

Neural attentional-filter mechanisms of listening success in middle-aged and older individuals

Successful listening crucially depends on intact attentional filters that separate relevant from irrelevant information. Research into their neurobiological implementation has focused on two potential auditory filter strategies: the lateralization of alpha power and selective neural speech tracking. However, the functional interplay of the two neural filter strategies and their potency to index listening success in an ageing population remains unclear. Using electroencephalography and a dual-talker task in a representative sample of listeners (N = 155; age=39-80 years), we here demonstrate an often-missed link from single-trial behavioural outcomes back to trial-by-trial changes in neural attentional filtering. First, we observe preserved attentional-cue-driven modulation of both neural filters across chronological age and hearing levels. Second, neural filter states vary independently of one another, demonstrating complementary neurobiological solutions of spatial selective attention. Stronger neural speech tracking but not alpha lateralization boosts trial-to-trial behavioural performance. Our results highlight the translational potential of neural speech tracking as an individualized neural marker of adaptive listening behaviour.

Listening to speech with a guinea pig-to-human brain-to-brain interface

Nicolelis wrote in his 2003 review on brain-machine interfaces (BMIs) that the design of a successful BMI relies on general physiological principles describing how neuronal signals are encoded. Our study explored whether neural information exchanged between brains of different species is possible, similar to the information exchange between computers. We show for the first time that single words processed by the guinea pig auditory system are intelligible to humans who receive the processed information via a cochlear implant. We recorded the neural response patterns to single-spoken words with multi-channel electrodes from the guinea inferior colliculus. The recordings served as a blueprint for trains of biphasic, charge-balanced electrical pulses, which a cochlear implant delivered to the cochlear implant user’s ear. Study participants completed a four-word forced-choice test and identified the correct word in 34.8% of trials. The participants' recognition, defined by the ability to choose the same word twice, whether right or wrong, was 53.6%. For all sessions, the participants received no training and no feedback. The results show that lexical information can be transmitted from an animal to a human auditory system. In the discussion, we will contemplate how learning from the animals might help developing novel coding strategies.

EEG-based auditory attention decoding using speech-level-based segmented computational models

Objective.Auditory attention in complex scenarios can be decoded by electroencephalography (EEG)-based cortical speech-envelope tracking. The relative root-mean-square (RMS) intensity is a valuable cue for the decomposition of speech into distinct characteristic segments. To improve auditory attention decoding (AAD) performance, this work proposed a novel segmented AAD approach to decode target speech envelopes from different RMS-level-based speech segments.Approach.Speech was decomposed into higher- and lower-RMS-level speech segments with a threshold of -10 dB relative RMS level. A support vector machine classifier was designed to identify higher- and lower-RMS-level speech segments, using clean target and mixed speech as reference signals based on corresponding EEG signals recorded when subjects listened to target auditory streams in competing two-speaker auditory scenes. Segmented computational models were developed with the classification results of higher- and lower-RMS-level speech segments. Speech envelopes were reconstructed based on segmented decoding models for either higher- or lower-RMS-level speech segments. AAD accuracies were calculated according to the correlations between actual and reconstructed speech envelopes. The performance of the proposed segmented AAD computational model was compared to those of traditional AAD methods with unified decoding functions.Main results.Higher- and lower-RMS-level speech segments in continuous sentences could be identified robustly with classification accuracies that approximated or exceeded 80% based on corresponding EEG signals at 6 dB, 3 dB, 0 dB, -3 dB and -6 dB signal-to-mask ratios (SMRs). Compared with unified AAD decoding methods, the proposed segmented AAD approach achieved more accurate results in the reconstruction of target speech envelopes and in the detection of attentional directions. Moreover, the proposed segmented decoding method had higher information transfer rates (ITRs) and shorter minimum expected switch times compared with the unified decoder.Significance.This study revealed that EEG signals may be used to classify higher- and lower-RMS-level-based speech segments across a wide range of SMR conditions (from 6 dB to -6 dB). A novel finding was that the specific information in different RMS-level-based speech segments facilitated EEG-based decoding of auditory attention. The significantly improved AAD accuracies and ITRs of the segmented decoding method suggests that this proposed computational model may be an effective method for the application of neuro-controlled brain-computer interfaces in complex auditory scenes.

Auditory stimulus-response modeling with a match-mismatch task

Objective.An auditory stimulus can be related to the brain response that it evokes by a stimulus-response model fit to the data. This offers insight into perceptual processes within the brain and is also of potential use for devices such as brain computer interfaces (BCIs). The quality of the model can be quantified by measuring the fit with a regression problem, or by applying it to a classification task and measuring its performance.Approach.Here we focus on amatch-mismatch(MM) task that entails deciding whether a segment of brain signal matches, via a model, the auditory stimulus that evoked it.Main results. Using these metrics, we describe a range of models of increasing complexity that we compare to methods in the literature, showing state-of-the-art performance. We document in detail one particular implementation, calibrated on a publicly-available database, that can serve as a robust reference to evaluate future developments.Significance.The MM task allows stimulus-response models to be evaluated in the limit of very high model accuracy, making it an attractive alternative to the more commonly used task of auditory attention detection. The MM task does not require class labels, so it is immune to mislabeling, and it is applicable to data recorded in listening scenarios with only one sound source, thus it is cheap to obtain large quantities of training and testing data. Performance metrics from this task, associated with regression accuracy, provide complementary insights into the relation between stimulus and response, as well as information about discriminatory power directly applicable to BCI applications.

Linguistic processing of task-irrelevant speech at a cocktail party

Paying attention to one speaker in a noisy place can be extremely difficult, because to-be-attended and task-irrelevant speech compete for processing resources. We tested whether this competition is restricted to acoustic-phonetic interference or if it extends to competition for linguistic processing as well. Neural activity was recorded using Magnetoencephalography as human participants were instructed to attend to natural speech presented to one ear, and task-irrelevant stimuli were presented to the other. Task-irrelevant stimuli consisted either of random sequences of syllables, or syllables structured to form coherent sentences, using hierarchical frequency-tagging. We find that the phrasal structure of structured task-irrelevant stimuli was represented in the neural response in left inferior frontal and posterior parietal regions, indicating that selective attention does not fully eliminate linguistic processing of task-irrelevant speech. Additionally, neural tracking of to-be-attended speech in left inferior frontal regions was enhanced when competing with structured task-irrelevant stimuli, suggesting inherent competition between them for linguistic processing.

Neural Decoding Reveals Concurrent Phonemic and Subphonemic Representations of Speech Across Tasks

Robust and efficient speech perception relies on the interpretation of acoustically variable phoneme realizations, yet prior neuroimaging studies are inconclusive regarding the degree to which subphonemic detail is maintained over time as categorical representations arise. It is also unknown whether this depends on the demands of the listening task. We addressed these questions by using neural decoding to quantify the (dis)similarity of brain response patterns evoked during two different tasks. We recorded magnetoencephalography (MEG) as adult participants heard isolated, randomized tokens from a /ba/-/da/ speech continuum. In the passive task, their attention was diverted. In the active task, they categorized each token as ba or da. We found that linear classifiers successfully decoded ba vs. da perception from the MEG data. Data from the left hemisphere were sufficient to decode the percept early in the trial, while the right hemisphere was necessary but not sufficient for decoding at later time points. We also decoded stimulus representations and found that they were maintained longer in the active task than in the passive task; however, these representations did not pattern more like discrete phonemes when an active categorical response was required. Instead, in both tasks, early phonemic patterns gave way to a representation of stimulus ambiguity that coincided in time with reliable percept decoding. Our results suggest that the categorization process does not require the loss of subphonemic detail, and that the neural representation of isolated speech sounds includes concurrent phonemic and subphonemic information.

Effects of Age on Cortical Tracking of Word-Level Features of Continuous Competing Speech

Speech-in-noise comprehension difficulties are common among the elderly population, yet traditional objective measures of speech perception are largely insensitive to this deficit, particularly in the absence of clinical hearing loss. In recent years, a growing body of research in young normal-hearing adults has demonstrated that high-level features related to speech semantics and lexical predictability elicit strong centro-parietal negativity in the EEG signal around 400 ms following the word onset. Here we investigate effects of age on cortical tracking of these word-level features within a two-talker speech mixture, and their relationship with self-reported difficulties with speech-in-noise understanding. While undergoing EEG recordings, younger and older adult participants listened to a continuous narrative story in the presence of a distractor story. We then utilized forward encoding models to estimate cortical tracking of four speech features: (1) word onsets, (2) "semantic" dissimilarity of each word relative to the preceding context, (3) lexical surprisal for each word, and (4) overall word audibility. Our results revealed robust tracking of all features for attended speech, with surprisal and word audibility showing significantly stronger contributions to neural activity than dissimilarity. Additionally, older adults exhibited significantly stronger tracking of word-level features than younger adults, especially over frontal electrode sites, potentially reflecting increased listening effort. Finally, neuro-behavioral analyses revealed trends of a negative relationship between subjective speech-in-noise perception difficulties and the model goodness-of-fit for attended speech, as well as a positive relationship between task performance and the goodness-of-fit, indicating behavioral relevance of these measures. Together, our results demonstrate the utility of modeling cortical responses to multi-talker speech using complex, word-level features and the potential for their use to study changes in speech processing due to aging and hearing loss.

Effects of Hearing Aid Noise Reduction on Early and Late Cortical Representations of Competing Talkers in Noise

OBJECTIVES

Previous research using non-invasive (magnetoencephalography, MEG) and invasive (electrocorticography, ECoG) neural recordings has demonstrated the progressive and hierarchical representation and processing of complex multi-talker auditory scenes in the auditory cortex. Early responses (<85 ms) in primary-like areas appear to represent the individual talkers with almost equal fidelity and are independent of attention in normal-hearing (NH) listeners. However, late responses (>85 ms) in higher-order non-primary areas selectively represent the attended talker with significantly higher fidelity than unattended talkers in NH and hearing-impaired (HI) listeners. Motivated by these findings, the objective of this study was to investigate the effect of a noise reduction scheme (NR) in a commercial hearing aid (HA) on the representation of complex multi-talker auditory scenes in distinct hierarchical stages of the auditory cortex by using high-density electroencephalography (EEG).

DESIGN

We addressed this issue by investigating early (<85 ms) and late (>85 ms) EEG responses recorded in 34 HI subjects fitted with HAs. The HA noise reduction (NR) was either on or off while the participants listened to a complex auditory scene. Participants were instructed to attend to one of two simultaneous talkers in the foreground while multi-talker babble noise played in the background (+3 dB SNR). After each trial, a two-choice question about the content of the attended speech was presented.

RESULTS

Using a stimulus reconstruction approach, our results suggest that the attention-related enhancement of neural representations of target and masker talkers located in the foreground, as well as suppression of the background noise in distinct hierarchical stages is significantly affected by the NR scheme. We found that the NR scheme contributed to the enhancement of the foreground and of the entire acoustic scene in the early responses, and that this enhancement was driven by better representation of the target speech. We found that the target talker in HI listeners was selectively represented in late responses. We found that use of the NR scheme resulted in enhanced representations of the target and masker speech in the foreground and a suppressed representation of the noise in the background in late responses. We found a significant effect of EEG time window on the strengths of the cortical representation of the target and masker.

CONCLUSION

Together, our analyses of the early and late responses obtained from HI listeners support the existing view of hierarchical processing in the auditory cortex. Our findings demonstrate the benefits of a NR scheme on the representation of complex multi-talker auditory scenes in different areas of the auditory cortex in HI listeners.

Measuring the cortical tracking of speech with optically-pumped magnetometers

During continuous speech listening, brain activity tracks speech rhythmicity at frequencies matching with the repetition rate of phrases (0.2-1.5 Hz), words (2-4 Hz) and syllables (4-8 Hz). Here, we evaluated the applicability of wearable MEG based on optically-pumped magnetometers (OPMs) to measure such cortical tracking of speech (CTS). Measuring CTS with OPMs is a priori challenging given the complications associated with OPM measurements at frequencies below 4 Hz, due to increased intrinsic interference and head movement artifacts. Still, this represents an important development as OPM-MEG provides lifespan compliance and substantially improved spatial resolution compared with classical MEG. In this study, four healthy right-handed adults listened to continuous speech for 9 min. The radial component of the magnetic field was recorded simultaneously with 45-46 OPMs evenly covering the scalp surface and fixed to an additively manufactured helmet which fitted all 4 participants. We estimated CTS with reconstruction accuracy and coherence, and determined the number of dominant principal components (PCs) to remove from the data (as a preprocessing step) for optimal estimation. We also identified the dominant source of CTS using a minimum norm estimate. CTS estimated with reconstruction accuracy and coherence was significant in all 4 participants at phrasal and word rates, and in 3 participants (reconstruction accuracy) or 2 (coherence) at syllabic rate. Overall, close-to-optimal CTS estimation was obtained when the 3 (reconstruction accuracy) or 10 (coherence) first PCs were removed from the data. Importantly, values of reconstruction accuracy (~0.4 for 0.2-1.5-Hz CTS and ~0.1 for 2-8-Hz CTS) were remarkably close to those previously reported in classical MEG studies. Finally, source reconstruction localized the main sources of CTS to bilateral auditory cortices. In conclusion, t his study demonstrates that OPMs can be used for the purpose of CTS assessment. This finding opens new research avenues to unravel the neural network involved in CTS across the lifespan and potential alterations in, e.g., language developmental disorders. Data also suggest that OPMs are generally suitable for recording neural activity at frequencies below 4 Hz provided PCA is used as a preprocessing step; 0.2-1.5-Hz being the lowest frequency range successfully investigated here.