Explaining flexible continuous speech comprehension from individual motor rhythms.

When speech is too fast, the tracking of the acoustic signal along the auditory pathway deteriorates, leading to suboptimal speech segmentation and decoding of speech information. Thus, speech comprehension is limited by the temporal constraints of the auditory system. Here we ask whether individual differences in auditory-motor coupling strength in part shape these temporal constraints. In two behavioural experiments, we characterize individual differences in the comprehension of naturalistic speech as function of the individual synchronization between the auditory and motor systems and the preferred frequencies of the systems. Obviously, speech comprehension declined at higher speech rates. Importantly, however, both higher auditory-motor synchronization and higher spontaneous speech motor production rates were predictive of better speech-comprehension performance. Furthermore, performance increased with higher working memory capacity (digit span) and higher linguistic, model-based sentence predictability-particularly so at higher speech rates and for individuals with high auditory-motor synchronization. The data provide evidence for a model of speech comprehension in which individual flexibility of not only the motor system but also auditory-motor synchronization may play a modulatory role.

Predicting Hearing aid Benefit Using Speech-Evoked Envelope Following Responses in Children With Hearing Loss.

Electroencephalography could serve as an objective tool to evaluate hearing aid benefit in infants who are developmentally unable to participate in hearing tests. We investigated whether speech-evoked envelope following responses (EFRs), a type of electroencephalography-based measure, could predict improved audibility with the use of a hearing aid in children with mild-to-severe permanent, mainly sensorineural, hearing loss. In 18 children, EFRs were elicited by six male-spoken band-limited phonemic stimuli--the first formants of /u/ and /i/, the second and higher formants of /u/ and /i/, and the fricatives /s/ and /∫/--presented together as /su∫i/. EFRs were recorded between the vertex and nape, when /su∫i/ was presented at 55, 65, and 75 dB SPL using insert earphones in unaided conditions and individually fit hearing aids in aided conditions. EFR amplitude and detectability improved with the use of a hearing aid, and the degree of improvement in EFR amplitude was dependent on the extent of change in behavioral thresholds between unaided and aided conditions. EFR detectability was primarily influenced by audibility; higher sensation level stimuli had an increased probability of detection. Overall EFR sensitivity in predicting audibility was significantly higher in aided (82.1%) than unaided conditions (66.5%) and did not vary as a function of stimulus or frequency. EFR specificity in ascertaining inaudibility was 90.8%. Aided improvement in EFR detectability was a significant predictor of hearing aid-facilitated change in speech discrimination accuracy. Results suggest that speech-evoked EFRs could be a useful objective tool in predicting hearing aid benefit in children with hearing loss.

Speech therapy to overcome dyslexia in primary schoolers.

This research was aimed to investigate changes in the reading technique and in terms of its semantic charge in primary schoolers diagnosed with dyslexia, which occur as a result of the integrated use of speech therapy techniques. The study was performed between 2016 and 2019 in 6 schools of Moscow and Almaty. It enrolled 194 and 200 children, respectively, who were examined with form I to III inclusive. The study revealed that 13% of children had reading speed disorders; they were constituted group 1. Another 11% had reading comprehension disorders; they constituted group 2. In group 1, by form III, the number of reading repetitions increased twofold. In group 2, the number of children, who read in words and phrases, increased by half; in group 1, it doubled. This research showed clear progress in children with technical dyslexia vs. those with semantic dyslexia. Based on the results, it is possible to develop a methodology for speech therapy techniques that can be suitable not only for speech therapists, but also for primary school teachers, as well as for parents of dyslectic children.

[A tool to assess functional communication skills and its correspondence with traditional language testing in patients with brain tumors]. / Instrument izmereniya funktsional'nykh kommunikativnykh navykov i sopostavlenie ego s traditsionnym rechevym testirovaniem u patsientov s opukholyami golovnogo mozga.

OBJECTIVE: To identify the relationship between speech impairment as measured by the Russian Aphasia Test (RAT) and functional communication as assessed by the Communicative Effectiveness Index (CETI). MATERIAL AND METHODS: RAT and CETI were administered to 87 patients at two time points, before surgery and in 3 months after brain tumor resection surgery. RESULTS: There were significant correlations between CETI and the total scores on RAT subtests for speech comprehension and production before surgery but not in the follow-up period. CONCLUSION: The present research is the first to present the Russian version of CETI and to confirm the relationship between speech disorders measured by the comprehensive standardized battery for evaluating speech function RAT and functional communication as measured by CETI.

Language and Psychosis: Tightening the Association.

This special issue of DISCOURSE in Psychosis focuses on the role of language in psychosis, including the relationships between formal thought disorder and conceptual disorganization, with speech and language markers and the neural mechanisms underlying these features in psychosis. It also covers the application of computational techniques in the study of language in psychosis, as well as the potential for using speech and language data for digital phenotyping in psychiatry.

Screening over Speech in Unselected Populations for Clinical Trials in AD (PROSPECT-AD): Study Design and Protocol.

BACKGROUND: Speech impairments are an early feature of Alzheimer's disease (AD) and consequently, analysing speech performance is a promising new digital biomarker for AD screening. Future clinical AD trials on disease modifying drugs will require a shift to very early identification of individuals at risk of dementia. Hence, digital markers of language and speech may offer a method for screening of at-risk populations that are at the earliest stages of AD, eventually in combination with advanced machine learning. To this end, we developed a screening battery consisting of speech-based neurocognitive tests. The automated test performs a remote primary screening using a simple telephone. OBJECTIVES: PROSPECT-AD aims to validate speech biomarkers for identification of individuals with early signs of AD and monitor their longitudinal course through access to well-phenotyped cohorts. DESIGN: PROSPECT-AD leverages ongoing cohorts such as EPAD (UK), DESCRIBE and DELCODE (Germany), and BioFINDER Primary Care (Sweden) and Beta-AARC (Spain) by adding a collection of speech data over the telephone to existing longitudinal follow-ups. Participants at risk of dementia are recruited from existing parent cohorts across Europe to form an AD 'probability-spectrum', i.e., individuals with a low risk to high risk of developing AD dementia. The characterization of cognition, biomarker and risk factor (genetic and environmental) status of each research participants over time combined with audio recordings of speech samples will provide a well-phenotyped population for comparing novel speech markers with current gold standard biomarkers and cognitive scores. PARTICIPANTS: N= 1000 participants aged 50 or older will be included in total, with a clinical dementia rating scale (CDR) score of 0 or 0.5. The study protocol is planned to run according to sites between 12 and 18 months. MEASUREMENTS: The speech protocol includes the following neurocognitive tests which will be administered remotely: Word List [Memory Function], Verbal Fluency [Executive Functions] and spontaneous free speech [Psychological and/ or behavioral symptoms]. Speech features on the linguistic and paralinguistic level will be extracted from the recordings and compared to data from CSF and blood biomarkers, neuroimaging, neuropsychological evaluations, genetic profiles, and family history. Primary candidate marker from speech will be a combination of most significant features in comparison to biomarkers as reference measure. Machine learning and computational techniques will be employed to identify the most significant speech biomarkers that could represent an early indicator of AD pathology. Furthermore, based on the analysis of speech performances, models will be trained to predict cognitive decline and disease progression across the AD continuum. CONCLUSION: The outcome of PROSPECT-AD may support AD drug development research as well as primary or tertiary prevention of dementia by providing a validated tool using a remote approach for identifying individuals at risk of dementia and monitoring individuals over time, either in a screening context or in clinical trials.

Coreference Delays in Psychotic Discourse: Widening the Temporal Window.

BACKGROUND AND HYPOTHESIS: Any form of coherent discourse depends on saying different things about the same entities at different times. Such recurrent references to the same entity need to predictably happen within certain temporal windows. We hypothesized that a failure of control over reference in speakers with schizophrenia (Sz) would become manifest through dynamic temporal measures. STUDY DESIGN: Conversational speech with a mean of 909.2 words (SD: 178.4) from 20 Chilean Spanish speakers with chronic Sz, 20 speakers at clinical high risk (CHR), and 20 controls were collected. Using directed speech graphs with referential noun phrases (NPs) as nodes, we studied deviances in the topology and temporal distribution of such NPs and of the entities they denote over narrative time. STUDY RESULTS: The Sz group had a larger density of NPs (number of NPs divided by total words) relative to both controls and CHR. This related to topological measures of distance between recurrent entities, which revealed that the Sz group produced more recurrences, as well as greater topological distances between them, relative to controls. A logistic regression using five topological measures showed that Sz and controls can be distinguished with 84.2% accuracy. CONCLUSIONS: This pattern indicates a widening of the temporal window in which entities are maintained in discourse and co-referenced in it. It substantiates and extends earlier evidence for deficits in the cognitive control over linguistic reference in psychotic discourse and informs both neurocognitive models of language in Sz and machine learning-based linguistic classifiers of psychotic speech.

Semantic Speech Networks Linked to Formal Thought Disorder in Early Psychosis.

BACKGROUND AND HYPOTHESIS: Mapping a patient's speech as a network has proved to be a useful way of understanding formal thought disorder in psychosis. However, to date, graph theory tools have not explicitly modelled the semantic content of speech, which is altered in psychosis. STUDY DESIGN: We developed an algorithm, "netts," to map the semantic content of speech as a network, then applied netts to construct semantic speech networks for a general population sample (N = 436), and a clinical sample comprising patients with first episode psychosis (FEP), people at clinical high risk of psychosis (CHR-P), and healthy controls (total N = 53). STUDY RESULTS: Semantic speech networks from the general population were more connected than size-matched randomized networks, with fewer and larger connected components, reflecting the nonrandom nature of speech. Networks from FEP patients were smaller than from healthy participants, for a picture description task but not a story recall task. For the former task, FEP networks were also more fragmented than those from controls; showing more connected components, which tended to include fewer nodes on average. CHR-P networks showed fragmentation values in-between FEP patients and controls. A clustering analysis suggested that semantic speech networks captured novel signals not already described by existing NLP measures. Network features were also related to negative symptom scores and scores on the Thought and Language Index, although these relationships did not survive correcting for multiple comparisons. CONCLUSIONS: Overall, these data suggest that semantic networks can enable deeper phenotyping of formal thought disorder in psychosis. Whilst here we focus on network fragmentation, the semantic speech networks created by Netts also contain other, rich information which could be extracted to shed further light on formal thought disorder. We are releasing Netts as an open Python package alongside this manuscript.

Latent Factors of Language Disturbance and Relationships to Quantitative Speech Features.

BACKGROUND AND HYPOTHESIS: Quantitative acoustic and textual measures derived from speech ("speech features") may provide valuable biomarkers for psychiatric disorders, particularly schizophrenia spectrum disorders (SSD). We sought to identify cross-diagnostic latent factors for speech disturbance with relevance for SSD and computational modeling. STUDY DESIGN: Clinical ratings for speech disturbance were generated across 14 items for a cross-diagnostic sample (N = 334), including SSD (n = 90). Speech features were quantified using an automated pipeline for brief recorded samples of free speech. Factor models for the clinical ratings were generated using exploratory factor analysis, then tested with confirmatory factor analysis in the cross-diagnostic and SSD groups. The relationships between factor scores and computational speech features were examined for 202 of the participants. STUDY RESULTS: We found a 3-factor model with a good fit in the cross-diagnostic group and an acceptable fit for the SSD subsample. The model identifies an impaired expressivity factor and 2 interrelated disorganized factors for inefficient and incoherent speech. Incoherent speech was specific to psychosis groups, while inefficient speech and impaired expressivity showed intermediate effects in people with nonpsychotic disorders. Each of the 3 factors had significant and distinct relationships with speech features, which differed for the cross-diagnostic vs SSD groups. CONCLUSIONS: We report a cross-diagnostic 3-factor model for speech disturbance which is supported by good statistical measures, intuitive, applicable to SSD, and relatable to linguistic theories. It provides a valuable framework for understanding speech disturbance and appropriate targets for modeling with quantitative speech features.

Language Analytics for Assessment of Mental Health Status and Functional Competency.

BACKGROUND AND HYPOTHESIS: Automated language analysis is becoming an increasingly popular tool in clinical research involving individuals with mental health disorders. Previous work has largely focused on using high-dimensional language features to develop diagnostic and prognostic models, but less work has been done to use linguistic output to assess downstream functional outcomes, which is critically important for clinical care. In this work, we study the relationship between automated language composites and clinical variables that characterize mental health status and functional competency using predictive modeling. STUDY DESIGN: Conversational transcripts were collected from a social skills assessment of individuals with schizophrenia (n = 141), bipolar disorder (n = 140), and healthy controls (n = 22). A set of composite language features based on a theoretical framework of speech production were extracted from each transcript and predictive models were trained. The prediction targets included clinical variables for assessment of mental health status and social and functional competency. All models were validated on a held-out test sample not accessible to the model designer. STUDY RESULTS: Our models predicted the neurocognitive composite with Pearson correlation PCC = 0.674; PANSS-positive with PCC = 0.509; PANSS-negative with PCC = 0.767; social skills composite with PCC = 0.785; functional competency composite with PCC = 0.616. Language features related to volition, affect, semantic coherence, appropriateness of response, and lexical diversity were useful for prediction of clinical variables. CONCLUSIONS: Language samples provide useful information for the prediction of a variety of clinical variables that characterize mental health status and functional competency.

Syntactic Network Analysis in Schizophrenia-Spectrum Disorders.

BACKGROUND: Language anomalies are a hallmark feature of schizophrenia-spectrum disorders (SSD). Here, we used network analysis to examine possible differences in syntactic relations between patients with SSD and healthy controls. Moreover, we assessed their relationship with sociodemographic factors, psychotic symptoms, and cognitive functioning, and we evaluated whether the quantification of syntactic network measures has diagnostic value. STUDY DESIGN: Using a semi-structured interview, we collected speech samples from 63 patients with SSD and 63 controls. Per sentence, a syntactic representation (ie, parse tree) was obtained and used as input for network analysis. The resulting syntactic networks were analyzed for 11 local and global network measures, which were compared between groups using multivariate analysis of covariance, considering the effects of age, sex, and education. RESULTS: Patients with SSD and controls significantly differed on most syntactic network measures. Sex had a significant effect on syntactic measures, and there was a significant interaction between sex and group, as the anomalies in syntactic relations were most pronounced in women with SSD. Syntactic measures were correlated with negative symptoms (Positive and Negative Syndrome Scale) and cognition (Brief Assessment of Cognition in Schizophrenia). A random forest classifier based on the best set of network features distinguished patients from controls with 74% cross-validated accuracy. CONCLUSIONS: Examining syntactic relations from a network perspective revealed robust differences between patients with SSD and healthy controls, especially in women. Our results support the validity of linguistic network analysis in SSD and have the potential to be used in combination with other automated language measures as a marker for SSD.

[Taal: een introductie]. / Taal: een introductie.

BACKGROUND: This special issue discusses the roles and functions of language in psychiatric practice from different perspectives. As an introduction, we discuss the phenomenon ‘language’ as an object of scientific investigation. AIM: To give a brief introduction to this theme issue. METHOD: After a terminological introduction and an outline of linguistics, we discuss some of the cognitive processes that enable humans to produce and interpret verbal utterances. RESULTS: The mental lexicon, the dictionary in our head, plays a central role in both language production and understanding. The starting point for language comprehension is recognizing basic form elements in the speech or sign stream (phonemes). Next, the perceiver must determine how words are related grammatically in order to deduce sentence meanings. We distinguish three successive steps in the production of language: conceptualizing, formulating and articulating. CONCLUSION: Production and understanding words and sentences rely on a complex interplay of cognitive processes. In communication, we use words and sentences to convey and recognize intentions. This requires close cooperation between interlocutors.

[Perspectives and practices of speech therapists regarding friendships after brain injury]. / Visie en werkzaamheden van logopedisten aangaande vriendschappen na hersen­letsel.

BACKGROUND: Acquired brain injury (ABI) is a common comorbidity in the psychiatric population. Consequences of ABI, including social communication problems, negatively affect friendships. However, current speech pathology practices regarding friendships after ABI remain unknown. AIM: To monitor perspectives, practices and facilitating as well as limiting factors with regard to these practices of Dutch speech therapists regarding friendships after ABI. METHOD: Survey study on whether, why, and how speech therapists do (not) perform work on friendships after ABI. RESULTS: Up to 90% of the 36 participating speech therapists believed that work related to friendships after ABI falls within the scope of their responsibilities. 78% of the speech therapists actually performed such activities. The most frequently mentioned facilitating factor in activities regarding friendship was the presence of supporting material, e.g. educational modules. The most frequently reported barrier was the very limited existence of social networks of persons with ABI. CONCLUSIONS: Work activities by speech therapists regarding friendships after ABI are numerous. Speech therapists are in need of material that can be used to support their work on friendships.

[Automated speech analysis can distinguish frontotemporal dementia from depression: a pilot study]. / Geautomatiseerde spraakanalyse bij onderscheiden van frontotemporale dementie en depressie.

BACKGROUND: Differentiating the behavioural variant of frontotemporal dementia from a depression is challenging. Recent development of automated speech analyses might add to diagnostic. AIM: To investigate the value of automated speech analyses in differentiating bvFTD from a depressive disorder. METHOD: A semistructured interview was recorded in 15 patients with bvFTD, 15 patients with a depressive disorder and 15 healthy controls, which was transcribed and analysed. Acoustic and semantic values were extracted and classified using machine learning. RESULTS: Acoustic values showed an 80% accuracy for differentiating bvFTD from depressive disorder and semantic values showed an 70.8% accuracy. CONCLUSION: Acoustic as well as semantic values show significant differences between bvFTD and depressive disorder. In automated speech analyses researches should consider privacy matters as well as possible confounders like age, sex and ethnicity. This study should be repeated in a larger population.

[Recognizing depression through computational language analysis]. / Herkennen van depressie met computationele taalanalyse.

BACKGROUND: Currently, clinical practice lacks a usable biomarker for the detection and differentiation of depression. Such a biomarker may be found in speech, from which important information can be distilled using automated speech analysis. AIM: To provide an overview of the fast-developing field of automated speech analysis for depression. METHOD: We summarize the current literature on speech features in depression. RESULTS: Current computational models can detect depression with high accuracy, rendering them applicable for diagnostic tools based on automatic speech analysis. Such tools are developing at a fast rate. CONCLUSION: Some challenges are still in the way of clinical implementation. For example, results differ largely between studies due to much variation in methodology. Furthermore, privacy and ethical issues need to be addressed before tools can be used.

Acoustic Analysis of Speech for Screening for Suicide Risk: Machine Learning Classifiers for Between- and Within-Person Evaluation of Suicidality.

BACKGROUND: Assessing a patient's suicide risk is challenging for health professionals because it depends on voluntary disclosure by the patient and often has limited resources. The application of novel machine learning approaches to determine suicide risk has clinical utility. OBJECTIVE: This study aimed to investigate cross-sectional and longitudinal approaches to assess suicidality based on acoustic voice features of psychiatric patients using artificial intelligence. METHODS: We collected 348 voice recordings during clinical interviews of 104 patients diagnosed with mood disorders at baseline and 2, 4, 8, and 12 months after recruitment. Suicidality was assessed using the Beck Scale for Suicidal Ideation and suicidal behavior using the Columbia Suicide Severity Rating Scale. The acoustic features of the voice, including temporal, formal, and spectral features, were extracted from the recordings. A between-person classification model that examines the vocal characteristics of individuals cross sectionally to detect individuals at high risk for suicide and a within-person classification model that detects considerable worsening of suicidality based on changes in acoustic features within an individual were developed and compared. Internal validation was performed using 10-fold cross validation of audio data from baseline to 2-month and external validation was performed using data from 2 to 4 months. RESULTS: A combined set of 12 acoustic features and 3 demographic variables (age, sex, and past suicide attempts) were included in the single-layer artificial neural network for the between-person classification model. Furthermore, 13 acoustic features were included in the extreme gradient boosting machine learning algorithm for the within-person model. The between-person classifier was able to detect high suicidality with 69% accuracy (sensitivity 74%, specificity 62%, area under the receiver operating characteristic curve 0.62), whereas the within-person model was able to predict worsening suicidality over 2 months with 79% accuracy (sensitivity 68%, specificity 84%, area under receiver operating characteristic curve 0.67). The second model showed 62% accuracy in predicting increased suicidality in external sets. CONCLUSIONS: Within-person analysis using changes in acoustic features within an individual is a promising approach to detect increased suicidality. Automated analysis of voice can be used to support the real-time assessment of suicide risk in primary care or telemedicine.

Differences in Sibilant Perception between Gender Expansive and Cisgender Individuals.

Acoustic cues of voice gender influence not only how people perceive the speaker's gender (e.g., whether that person is a man, woman, or non-binary) but also how they perceive certain phonemes produced by that person. One such sociophonetic cue is the [s]/[ʃ] distinction in English; which phoneme is perceived depends on the perceived gender of the speaker. Recent research has shown that gender expansive people differ from cisgender people in their perception of voice gender and thus, this could be reflected in their categorization of sibilants. Despite this, there has been no research to date on how gender expansive people categorize sibilants. Furthermore, while voice gender expression is often discussed within a biological context (e.g., vocal folds), voice extends to those who use other communication methods. The current study fills this gap by explicitly recruiting people of all genders and asking them to perform a sibilant categorization task using synthetic voices. The results show that cisgender and gender expansive people perceive synthetic sibilants differently, especially from a "nonbinary" synthetic voice. These results have implications for developing more inclusive speech technology for gender expansive individuals, in particular for nonbinary people who use speech-generating devices.

Exposure to hate speech deteriorates neurocognitive mechanisms of the ability to understand others' pain.

The widespread ubiquity of hate speech affects people's attitudes and behavior. Exposure to hate speech can lead to prejudice, dehumanization, and lack of empathy towards members of outgroups. However, the impact of exposure to hate speech on empathy and propensity to attribute mental states to others has never been directly tested empirically. In this fMRI study, we examine the effects of exposure to hate speech on neural mechanisms of empathy towards ingroup (Poles) versus outgroup members (Arabs). Thirty healthy young adults were randomly assigned to 2 groups: hateful and neutral. During the fMRI study, they were initially exposed to hateful or neutral comments and subsequently to narratives depicting Poles and Arabs in pain. Using whole-brain and region of interest analysis, we showed that exposure to derogatory language about migrants attenuates the brain response to someone else's pain in the right temporal parietal junction (rTPJ), irrespective of group membership (Poles or Arabs). Given that rTPJ is associated with processes relevant to perspective-taking, its reduced activity might be related to a decreased propensity to take the psychological perspective of others. This finding suggests that hate speech affects human functioning beyond intergroup relations.

Can object identification difficulty be predicted based on disfluencies and eye-movements in connected speech?

In the current study, we asked whether delays in the earliest stages of picture naming elicit disfluency. To address this question, we used a network task, where participants describe the route taken by a marker through visually presented networks of objects. Additionally, given that disfluencies are arguably multifactorial, we combined this task with eye tracking, to be able to disentangle disfluency related to word preparation from other factors (e.g., stalling strategy). We used visual blurring, which hinders visual identification of the items and thereby slows down selection of a lexical concept. We tested the effect of this manipulation on disfluency production and visual attention. Blurriness did not lead to more disfluency on average and viewing times decreased with blurred pictures. However, multivariate pattern analyses revealed that a classifier could predict above chance, from the pattern of disfluency, whether each participant was about to name blurred or control pictures. Impeding the conceptual generation of a message therefore affected the pattern of disfluencies of each participant individually, but this pattern was not consistent from one participant to another. Additionally, some of the disfluency and eye-movement variables correlated with individual cognitive differences, in particular with inhibition.