Cortical encoding of speech enhances task-relevant acoustic information

A behavioural exploration of language aptitude and experience, cognition and more using Graph Analysis

Language aptitude has recently regained interest in cognitive neuroscience. Traditional language aptitude testing included phonemic coding ability, associative memory, grammatical sensitivity and inductive language learning. Moreover, domain-general cognitive abilities are associated with individual differences in language aptitude, together with factors that have yet to be elucidated. Beyond domain-general cognition, it is also likely that aptitude and experience in domain-specific but non-linguistic fields (e.g. music or numerical processing) influence and are influenced by language aptitude. We investigated some of these relationships in a sample of 152 participants, using exploratory graph analysis, across different levels of regularisation, i.e. sensitivity. We carried out a meta cluster analysis in a second step to identify variables that are robustly grouped together. We discuss the data, as well as their meta-network groupings, at a baseline network sensitivity level, and in two analyses, one including and the other excluding dyslexic readers. Our results show a stable association between language and cognition, and the isolation of multilingual language experience, musicality and literacy. We highlight the necessity of a more comprehensive view of language and of cognition as multivariate systems.

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.

Cortical-striatal brain network distinguishes deepfake from real speaker identity

Deepfakes are viral ingredients of digital environments, and they can trick human cognition into misperceiving the fake as real. Here, we test the neurocognitive sensitivity of 25 participants to accept or reject person identities as recreated in audio deepfakes. We generate high-quality voice identity clones from natural speakers by using advanced deepfake technologies. During an identity matching task, participants show intermediate performance with deepfake voices, indicating levels of deception and resistance to deepfake identity spoofing. On the brain level, univariate and multivariate analyses consistently reveal a central cortico-striatal network that decoded the vocal acoustic pattern and deepfake-level (auditory cortex), as well as natural speaker identities (nucleus accumbens), which are valued for their social relevance. This network is embedded in a broader neural identity and object recognition network. Humans can thus be partly tricked by deepfakes, but the neurocognitive mechanisms identified during deepfake processing open windows for strengthening human resilience to fake information.

Linguistic modulation of the neural encoding of phonemes

Speech comprehension entails the neural mapping of the acoustic speech signal onto learned linguistic units. This acousto-linguistic transformation is bi-directional, whereby higher-level linguistic processes (e.g. semantics) modulate the acoustic analysis of individual linguistic units. Here, we investigated the cortical topography and linguistic modulation of the most fundamental linguistic unit, the phoneme. We presented natural speech and "phoneme quilts" (pseudo-randomly shuffled phonemes) in either a familiar (English) or unfamiliar (Korean) language to native English speakers while recording functional magnetic resonance imaging. This allowed us to dissociate the contribution of acoustic vs. linguistic processes toward phoneme analysis. We show that (i) the acoustic analysis of phonemes is modulated by linguistic analysis and (ii) that for this modulation, both of acoustic and phonetic information need to be incorporated. These results suggest that the linguistic modulation of cortical sensitivity to phoneme classes minimizes prediction error during natural speech perception, thereby aiding speech comprehension in challenging listening situations.

Spectrotemporal cues and attention jointly modulate fMRI network topology for sentence and melody perception

Speech and music are two fundamental modes of human communication. Lateralisation of key processes underlying their perception has been related both to the distinct sensitivity to low-level spectrotemporal acoustic features and to top-down attention. However, the interplay between bottom-up and top-down processes needs to be clarified. In the present study, we investigated the contribution of acoustics and attention to melodies or sentences to lateralisation in fMRI functional network topology. We used sung speech stimuli selectively filtered in temporal or spectral modulation domains with crossed and balanced verbal and melodic content. Perception of speech decreased with degradation of temporal information, whereas perception of melodies decreased with spectral degradation. Applying graph theoretical metrics on fMRI connectivity matrices, we found that local clustering, reflecting functional specialisation, linearly increased when spectral or temporal cues crucial for the task goal were incrementally degraded. These effects occurred in a bilateral fronto-temporo-parietal network for processing temporally degraded sentences and in right auditory regions for processing spectrally degraded melodies. In contrast, global topology remained stable across conditions. These findings suggest that lateralisation for speech and music partially depends on an interplay of acoustic cues and task goals under increased attentional demands.

A convolutional neural network provides a generalizable model of natural sound coding by neural populations in auditory cortex

Convolutional neural networks (CNNs) can provide powerful and flexible models of neural sensory processing. However, the utility of CNNs in studying the auditory system has been limited by their requirement for large datasets and the complex response properties of single auditory neurons. To address these limitations, we developed a population encoding model: a CNN that simultaneously predicts activity of several hundred neurons recorded during presentation of a large set of natural sounds. This approach defines a shared spectro-temporal space and pools statistical power across neurons. Population models of varying architecture performed consistently and substantially better than traditional linear-nonlinear models on data from primary and non-primary auditory cortex. Moreover, population models were highly generalizable. The output layer of a model pre-trained on one population of neurons could be fit to data from novel single units, achieving performance equivalent to that of neurons in the original fit data. This ability to generalize suggests that population encoding models capture a complete representational space across neurons in an auditory cortical field.

On the encoding of natural music in computational models and human brains

This article discusses recent developments and advances in the neuroscience of music to understand the nature of musical emotion. In particular, it highlights how system identification techniques and computational models of music have advanced our understanding of how the human brain processes the textures and structures of music and how the processed information evokes emotions. Musical models relate physical properties of stimuli to internal representations called features, and predictive models relate features to neural or behavioral responses and test their predictions against independent unseen data. The new frameworks do not require orthogonalized stimuli in controlled experiments to establish reproducible knowledge, which has opened up a new wave of naturalistic neuroscience. The current review focuses on how this trend has transformed the domain of the neuroscience of music.

How Learning to Read Changes the Listening Brain

Reading acquisition reorganizes existing brain networks for speech and visual processing to form novel audio-visual language representations. This requires substantial cortical plasticity that is reflected in changes in brain activation and functional as well as structural connectivity between brain areas. The extent to which a child's brain can accommodate these changes may underlie the high variability in reading outcome in both typical and dyslexic readers. In this review, we focus on reading-induced functional changes of the dorsal speech network in particular and discuss how its reciprocal interactions with the ventral reading network contributes to reading outcome. We discuss how the dynamic and intertwined development of both reading networks may be best captured by approaching reading from a skill learning perspective, using audio-visual learning paradigms and longitudinal designs to follow neuro-behavioral changes while children's reading skills unfold.

Frontotemporal activation differs between perception of simulated cochlear implant speech and speech in background noise: An image-based fNIRS study

In this study we used functional near-infrared spectroscopy (fNIRS) to investigate neural responses in normal-hearing adults as a function of speech recognition accuracy, intelligibility of the speech stimulus, and the manner in which speech is distorted. Participants listened to sentences and reported aloud what they heard. Speech quality was distorted artificially by vocoding (simulated cochlear implant speech) or naturally by adding background noise. Each type of distortion included high and low-intelligibility conditions. Sentences in quiet were used as baseline comparison. fNIRS data were analyzed using a newly developed image reconstruction approach. First, elevated cortical responses in the middle temporal gyrus (MTG) and middle frontal gyrus (MFG) were associated with speech recognition during the low-intelligibility conditions. Second, activation in the MTG was associated with recognition of vocoded speech with low intelligibility, whereas MFG activity was largely driven by recognition of speech in background noise, suggesting that the cortical response varies as a function of distortion type. Lastly, an accuracy effect in the MFG demonstrated significantly higher activation during correct perception relative to incorrect perception of speech. These results suggest that normal-hearing adults (i.e., untrained listeners of vocoded stimuli) do not exploit the same attentional mechanisms of the frontal cortex used to resolve naturally degraded speech and may instead rely on segmental and phonetic analyses in the temporal lobe to discriminate vocoded speech.

Attentional Modulation of Hierarchical Speech Representations in a Multitalker Environment

Humans are remarkably adept in listening to a desired speaker in a crowded environment, while filtering out nontarget speakers in the background. Attention is key to solving this difficult cocktail-party task, yet a detailed characterization of attentional effects on speech representations is lacking. It remains unclear across what levels of speech features and how much attentional modulation occurs in each brain area during the cocktail-party task. To address these questions, we recorded whole-brain blood-oxygen-level-dependent (BOLD) responses while subjects either passively listened to single-speaker stories, or selectively attended to a male or a female speaker in temporally overlaid stories in separate experiments. Spectral, articulatory, and semantic models of the natural stories were constructed. Intrinsic selectivity profiles were identified via voxelwise models fit to passive listening responses. Attentional modulations were then quantified based on model predictions for attended and unattended stories in the cocktail-party task. We find that attention causes broad modulations at multiple levels of speech representations while growing stronger toward later stages of processing, and that unattended speech is represented up to the semantic level in parabelt auditory cortex. These results provide insights on attentional mechanisms that underlie the ability to selectively listen to a desired speaker in noisy multispeaker environments.

Auditory cortical micro-networks show differential connectivity during voice and speech processing in humans

The temporal voice areas (TVAs) in bilateral auditory cortex (AC) appear specialized for voice processing. Previous research assumed a uniform functional profile for the TVAs which are broadly spread along the bilateral AC. Alternatively, the TVAs might comprise separate AC nodes controlling differential neural functions for voice and speech decoding, organized as local micro-circuits. To investigate micro-circuits, we modeled the directional connectivity between TVA nodes during voice processing in humans while acquiring brain activity using neuroimaging. Results show several bilateral AC nodes for general voice decoding (speech and non-speech voices) and for speech decoding in particular. Furthermore, non-hierarchical and differential bilateral AC networks manifest distinct excitatory and inhibitory pathways for voice and speech processing. Finally, while voice and speech processing seem to have distinctive but integrated neural circuits in the left AC, the right AC reveals disintegrated neural circuits for both sounds. Altogether, we demonstrate a functional heterogeneity in the TVAs for voice decoding based on local micro-circuits.

Generalizable dimensions of human cortical auditory processing of speech in natural soundscapes: A data-driven ultra high field fMRI approach

Speech comprehension in natural soundscapes rests on the ability of the auditory system to extract speech information from a complex acoustic signal with overlapping contributions from many sound sources. Here we reveal the canonical processing of speech in natural soundscapes on multiple scales by using data-driven modeling approaches to characterize sounds to analyze ultra high field fMRI recorded while participants listened to the audio soundtrack of a movie. We show that at the functional level the neuronal processing of speech in natural soundscapes can be surprisingly low dimensional in the human cortex, highlighting the functional efficiency of the auditory system for a seemingly complex task. Particularly, we find that a model comprising three functional dimensions of auditory processing in the temporal lobes is shared across participants' fMRI activity. We further demonstrate that the three functional dimensions are implemented in anatomically overlapping networks that process different aspects of speech in natural soundscapes. One is most sensitive to complex auditory features present in speech, another to complex auditory features and fast temporal modulations, that are not specific to speech, and one codes mainly sound level. These results were derived with few a-priori assumptions and provide a detailed and computationally reproducible account of the cortical activity in the temporal lobe elicited by the processing of speech in natural soundscapes.

Categorizing human vocal signals depends on an integrated auditory-frontal cortical network

Voice signals are relevant for auditory communication and suggested to be processed in dedicated auditory cortex (AC) regions. While recent reports highlighted an additional role of the inferior frontal cortex (IFC), a detailed description of the integrated functioning of the AC-IFC network and its task relevance for voice processing is missing. Using neuroimaging, we tested sound categorization while human participants either focused on the higher-order vocal-sound dimension (voice task) or feature-based intensity dimension (loudness task) while listening to the same sound material. We found differential involvements of the AC and IFC depending on the task performed and whether the voice dimension was of task relevance or not. First, when comparing neural vocal-sound processing of our task-based with previously reported passive listening designs we observed highly similar cortical activations in the AC and IFC. Second, during task-based vocal-sound processing we observed voice-sensitive responses in the AC and IFC whereas intensity processing was restricted to distinct AC regions. Third, the IFC flexibly adapted to the vocal-sounds' task relevance, being only active when the voice dimension was task relevant. Forth and finally, connectivity modeling revealed that vocal signals independent of their task relevance provided significant input to bilateral AC. However, only when attention was on the voice dimension, we found significant modulations of auditory-frontal connections. Our findings suggest an integrated auditory-frontal network to be essential for behaviorally relevant vocal-sounds processing. The IFC seems to be an important hub of the extended voice network when representing higher-order vocal objects and guiding goal-directed behavior.

Speech signal analysis of alzheimer's diseases in farsi using auditory model system

In recent years, extensive studies have been conducted on the diagnosis of Alzheimer's disease (AD) using the non-invasive speech signal recognition method. In this study, Farsi speech signals were analyzed using the auditory model system (AMS) in order to recognize AD. For this purpose, after the pre-processing of the speech signals and utilizing AMS, 4D outputs as function of time, frequency, rate, and scale range were obtained. The AMS outcomes were averaged in term of time to analyze the rate-frequency-scale for both groups, Alzheimer's and healthy control subjects. Thereafter, the maximum of spectral and temporal modulation and frequency were extracted to classify by the support vector machine (SVM). The SVM achieves higher promising recognition accuracy with compare to prevalent approaches in the field of speech processing. The acceptable results demonstrate the applicability of the proposed algorithm in non-invasive and low-cost recognizing Alzheimer's only using the few extracted features of the speech signal.

Neural Correlates of Phonetic Adaptation as Induced by Lexical and Audiovisual Context

When speech perception is difficult, one way listeners adjust is by reconfiguring phoneme category boundaries, drawing on contextual information. Both lexical knowledge and lipreading cues are used in this way, but it remains unknown whether these two differing forms of perceptual learning are similar at a neural level. This study compared phoneme boundary adjustments driven by lexical or audiovisual cues, using ultra-high-field 7-T fMRI. During imaging, participants heard exposure stimuli and test stimuli. Exposure stimuli for lexical retuning were audio recordings of words, and those for audiovisual recalibration were audio-video recordings of lip movements during utterances of pseudowords. Test stimuli were ambiguous phonetic strings presented without context, and listeners reported what phoneme they heard. Reports reflected phoneme biases in preceding exposure blocks (e.g., more reported /p/ after /p/-biased exposure). Analysis of corresponding brain responses indicated that both forms of cue use were associated with a network of activity across the temporal cortex, plus parietal, insula, and motor areas. Audiovisual recalibration also elicited significant occipital cortex activity despite the lack of visual stimuli. Activity levels in several ROIs also covaried with strength of audiovisual recalibration, with greater activity accompanying larger recalibration shifts. Similar activation patterns appeared for lexical retuning, but here, no significant ROIs were identified. Audiovisual and lexical forms of perceptual learning thus induce largely similar brain response patterns. However, audiovisual recalibration involves additional visual cortex contributions, suggesting that previously acquired visual information (on lip movements) is retrieved and deployed to disambiguate auditory perception.

The Dialog of Primary and Non-primary Auditory Cortex at the 'Cocktail Party'

In this issue of Neuron, O'Sullivan et al. (2019) measured electro-cortical responses to "cocktail party" speech mixtures in neurosurgical patients and demonstrated that the selective enhancement of attended speech is achieved through the adaptive weighting of primary auditory cortex output by non-primary auditory cortex.

TASH: Toolbox for the Automated Segmentation of Heschl's gyrus

Auditory cortex volume and shape differences have been observed in the context of phonetic learning, musicianship and dyslexia. Heschl’s gyrus, which includes primary auditory cortex, displays large anatomical variability across individuals and hemispheres. Given this variability, manual labelling is the gold standard for segmenting HG, but is time consuming and error prone. Our novel toolbox, called ‘Toolbox for the Automated Segmentation of HG’ or TASH, automatically segments HG in brain structural MRI data, and extracts measures including its volume, surface area and cortical thickness. TASH builds upon FreeSurfer, which provides an initial segmentation of auditory regions, and implements further steps to perform finer auditory cortex delineation. We validate TASH by showing significant relationships between HG volumes obtained using manual labelling and using TASH, in three independent datasets acquired on different scanners and field strengths, and by showing good qualitative segmentation. We also present two applications of TASH, demonstrating replication and extension of previously published findings of relationships between HG volumes and (a) phonetic learning, and (b) musicianship. In sum, TASH effectively segments HG in a fully automated and reproducible manner, opening up a wide range of applications in the domains of expertise, disease, genetics and brain plasticity.