Silent Pauses and Speech Indices as Biomarkers for Primary Progressive Aphasia

Speech pauses in speakers with and without aphasia: A usage-based approach

Pauses in speech are indicators of cognitive effort during language production and have been examined to inform theories of lexical, grammatical and discourse processing in healthy speakers and individuals with aphasia (IWA). Studies of pauses have commonly focused on their location and duration in relation to grammatical properties such as word class or phrase complexity. However, recent studies of speech output in aphasia have revealed that utterances of IWA are characterised by stronger collocations, i.e., combinations of words that are often used together. We investigated the effects of collocation strength and lexical frequency on pause duration in comic strip narrations of IWA and non-brain-damaged (NBD) individuals with part of speech (PoS; content and function words) as covariate. Both groups showed a decrease in pause duration within more strongly collocated bigrams and before more frequent content words, with stronger effects in IWA. These results are consistent with frameworks which propose that strong collocations are more likely to be processed as holistic, perhaps even word-like, units. Usage-based approaches prove valuable in explaining patterns of preservation and impairment in aphasic language production.

Dysfluency in primary progressive aphasia: Temporal speech parameters

Analysing spontaneous speech in individuals experiencing fluency difficulties holds potential for diagnosing speech and language disorders, including Primary Progressive Aphasia (PPA). Dysfluency in the spontaneous speech of patients with PPA has mostly been described in terms of abnormal pausing behaviour, but the temporal features related to speech have drawn little attention. This study compares speech-related fluency parameters in the three main variants of PPA and in typical speech. Forty-three adults participated in this research, thirteen with the logopenic variant of PPA (lvPPA), ten with the non-fluent variant (nfvPPA), nine with the semantic variant (svPPA), and eleven who were healthy age-matched adults. Participants' fluency was assessed through a picture description task from which 42 parameters were computed including syllable duration, speaking pace, the duration of speech chunks (i.e. interpausal units, IPU), and the number of linguistic units per IPU and per second. The results showed that each PPA variant exhibited abnormal speech characteristics reflecting various underlying factors, from motor speech deficits to higher-level issues. Out of the 42 parameters considered, 37 proved useful for characterising dysfluency in the three main PPA variants and 35 in distinguishing among them. Therefore, taking into account not only pausing behaviour but also temporal speech parameters can provide a fuller understanding of dysfluency in PPA. However, no single parameter by itself sufficed to distinguish one PPA group from the other two, further evidence that dysfluency is not dichotomous but rather multidimensional, and that complementary multiparametric analyses are needed.

A Methodological Approach to Quantifying Silent Pauses, Speech Rate, and Articulation Rate across Distinct Narrative Tasks: Introducing the Connected Speech Analysis Protocol (CSAP)

The examination of connected speech may serve as a valuable tool for exploring speech output in both healthy speakers and individuals with language disorders. Numerous studies incorporate various fluency and silence measures into their analyses to investigate speech output patterns in different populations, along with the underlying cognitive processes that occur while speaking. However, methodological inconsistencies across existing studies pose challenges in comparing their results. In the current study, we introduce CSAP (Connected Speech Analysis Protocol), which is a specific methodological approach to investigate fluency metrics, such as articulation rate and speech rate, as well as silence measures, including silent pauses' frequency and duration. We emphasize the importance of employing a comprehensive set of measures within a specific methodological framework to better understand speech output patterns. Additionally, we advocate for the use of distinct narrative tasks for a thorough investigation of speech output in different conditions. We provide an example of data on which we implement CSAP to showcase the proposed pipeline. In conclusion, CSAP offers a comprehensive framework for investigating speech output patterns, incorporating fluency metrics and silence measures in distinct narrative tasks, thus allowing a detailed quantification of connected speech in both healthy and clinical populations. We emphasize the significance of adopting a unified methodological approach in connected speech studies, enabling the integration of results for more robust and generalizable conclusions.

Investigating silent pauses in connected speech: integrating linguistic, neuropsychological, and neuroanatomical perspectives across narrative tasks in post-stroke aphasia

Introduction

Silent pauses are regarded as integral components of the temporal organization of speech. However, it has also been hypothesized that they serve as markers for internal cognitive processes, including word access, monitoring, planning, and memory functions. Although existing evidence across various pathological populations underscores the importance of investigating silent pauses' characteristics, particularly in terms of frequency and duration, there is a scarcity of data within the domain of post-stroke aphasia.

Methods

The primary objective of the present study is to scrutinize the frequency and duration of silent pauses in two distinct narrative tasks within a cohort of 32 patients with chronic post-stroke aphasia, in comparison with a control group of healthy speakers. Subsequently, we investigate potential correlation patterns between silent pause measures, i.e., frequency and duration, across the two narrative tasks within the patient group, their performance in neuropsychological assessments, and lesion data.

Results

Our findings showed that patients exhibited a higher frequency of longer-duration pauses in both narrative tasks compared to healthy speakers. Furthermore, within-group comparisons revealed that patients tended to pause more frequently and for longer durations in the picture description task, while healthy participants exhibited the opposite trend. With regard to our second research question, a marginally significant interaction emerged between performance in semantic verbal fluency and the narrative task, in relation to the location of silent pauses-whether between or within clauses-predicting the duration of silent pauses in the patient group. However, no significant results were observed for the frequency of silent pauses. Lastly, our study identified that the duration of silent pauses could be predicted by distinct Regions of Interest (ROIs) in spared tissue within the left hemisphere, as a function of the narrative task.

Discussion

Overall, this study follows an integrative approach of linguistic, neuropsychological and neuroanatomical data to define silent pauses in connected speech, and illustrates interrelations between cognitive components, temporal aspects of speech, and anatomical indices, while it further highlights the importance of studying connected speech indices using different narrative tasks.

Efficient Pause Extraction and Encode Strategy for Alzheimer's Disease Detection Using Only Acoustic Features from Spontaneous Speech

Clinical studies have shown that speech pauses can reflect the cognitive function differences between Alzheimer's Disease (AD) and non-AD patients, while the value of pause information in AD detection has not been fully explored. Herein, we propose a speech pause feature extraction and encoding strategy for only acoustic-signal-based AD detection. First, a voice activity detection (VAD) method was constructed to detect pause/non-pause feature and encode it to binary pause sequences that are easier to calculate. Then, an ensemble machine-learning-based approach was proposed for the classification of AD from the participants' spontaneous speech, based on the VAD Pause feature sequence and common acoustic feature sets (ComParE and eGeMAPS). The proposed pause feature sequence was verified in five machine-learning models. The validation data included two public challenge datasets (ADReSS and ADReSSo, English voice) and a local dataset (10 audio recordings containing five patients and five controls, Chinese voice). Results showed that the VAD Pause feature was more effective than common feature sets (ComParE: 6373 features and eGeMAPS: 88 features) for AD classification, and that the ensemble method improved the accuracy by more than 5% compared to several baseline methods (8% on the ADReSS dataset; 5.9% on the ADReSSo dataset). Moreover, the pause-sequence-based AD detection method could achieve 80% accuracy on the local dataset. Our study further demonstrated the potential of pause information in speech-based AD detection, and also contributed to a more accessible and general pause feature extraction and encoding method for AD detection.