Associations of acoustically measured tongue/jaw movements and portion of time speaking with negative symptom severity in patients with schizophrenia in Italy and the United States

Bernardini, Francesco; Lunden, Anya; Covington, Michael A.; Broussard, Beth; Halpern, B; Alolayan, Yazeed; Crisafio, Anthony; Pauselli, Luca; Balducci, Pierfrancesco Maria; Capulong, Leslie; Attademo, Luigi; Lucarini, Emanuela; Salierno, Gianfranco; Natalicchi, Luca; Quartesan, Roberto; Compton, Michael T.

doi:10.1016/j.psychres.2016.03.037

Cited by 26 publications

(18 citation statements)

References 13 publications

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“…There are very few publications focusing on predicting symptoms or problems. However, it would be desirable to link acoustic features to specific symptoms or problems that may be shared across disorders by detecting specific subitems within diagnostic questionnaires in line with the NIMH RDoC described in Section 1 (eg, Reference ; for further discussion, see Reference ). In psychiatry, there is a current trend to move from symptoms, which assume an underlying latent disease or disorder, to problems (eg, less sleep, lower energy), which may be related to underlying biological mechanisms.…”

Section: Discussionmentioning

confidence: 99%

Automated assessment of psychiatric disorders using speech: A systematic review

Low

Bentley

Ghosh

2020

Laryngoscope Investig Oto

298

268

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Objective There are many barriers to accessing mental health assessments including cost and stigma. Even when individuals receive professional care, assessments are intermittent and may be limited partly due to the episodic nature of psychiatric symptoms. Therefore, machine‐learning technology using speech samples obtained in the clinic or remotely could one day be a biomarker to improve diagnosis and treatment. To date, reviews have only focused on using acoustic features from speech to detect depression and schizophrenia. Here, we present the first systematic review of studies using speech for automated assessments across a broader range of psychiatric disorders. Methods We followed the Preferred Reporting Items for Systematic Reviews and Meta‐Analysis (PRISMA) guidelines. We included studies from the last 10 years using speech to identify the presence or severity of disorders within the Diagnostic and Statistical Manual of Mental Disorders (DSM‐5). For each study, we describe sample size, clinical evaluation method, speech‐eliciting tasks, machine learning methodology, performance, and other relevant findings. Results 1395 studies were screened of which 127 studies met the inclusion criteria. The majority of studies were on depression, schizophrenia, and bipolar disorder, and the remaining on post‐traumatic stress disorder, anxiety disorders, and eating disorders. 63% of studies built machine learning predictive models, and the remaining 37% performed null‐hypothesis testing only. We provide an online database with our search results and synthesize how acoustic features appear in each disorder. Conclusion Speech processing technology could aid mental health assessments, but there are many obstacles to overcome, especially the need for comprehensive transdiagnostic and longitudinal studies. Given the diverse types of data sets, feature extraction, computational methodologies, and evaluation criteria, we provide guidelines for both acquiring data and building machine learning models with a focus on testing hypotheses, open science, reproducibility, and generalizability. Level of Evidence 3a

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Section: Discussionmentioning

confidence: 99%

Automated assessment of psychiatric disorders using speech: A systematic review

Low

Bentley

Ghosh

2020

Laryngoscope Investig Oto

298

268

View full text Add to dashboard Cite

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“…Researchers have found that speakers with schizophrenia displayed an 11% reduction in vowel space (i.e., a reduction of tongue height captured by first formant-F1) [11], [12]. In addition, vowel space features have also been found to be informative for predicting emotional states (e.g.…”

Section: Vowel Spacementioning

confidence: 99%

“…Previous research in acoustic and prosodic features of psychosis has been mostly focused on schizophrenia. Some of the relevant findings of this research are the detection of impaired intonation contours in individuals that present negative symptoms; their increased pause time [10], and reduced vowel space [11], [12]; and the differences in Mel-frequency cepstral coefficients (MFCC) between healthy controls and schizophrenia patients. With respect to psychosis, researchers have found that prosody is affected with an increase number of pauses and many hesitations than in normal speech, and first episode psychotic patients displaying significant linguistic and emotional prosody deficits compared to healthy controls [13].…”

Section: Introductionmentioning

confidence: 99%

Analyzing acoustic and prosodic fluctuations in free speech to predict psychosis onset in high-risk youths

Agurto

Pietrowicz

Norel

et al. 2020

2020 42nd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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The diagnosis and treatment of psychiatric disorders depends on the analysis of behavior through language by a clinical specialist. This analysis is subjective in nature and could benefit from automated, objective acoustic and linguistic processing methods. This integrated approach would convey a richer representation of patient speech, particularly for expression of emotion. In this work, we explore the potential of acoustic and prosodic metrics to infer clinical variables and predict psychosis, a condition which produces measurable derailment and tangentiality in patient language. To that purpose, we analyzed the recordings of 32 young patients at high risk of developing clinical psychosis.

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“…Compared to that of healthy control subjects, the speech of patients on the schizophrenia spectrum appears less fluent (40), contains more and longer pauses (44,47) as well as less pitch variability (measured as the variance of fundamental frequency for each syllable) (40,43). Although prosodic parameters were associated neither with antipsychotic dosage (38,47) nor with positive symptoms (46,47), an association with negative symptoms was found (38,40,46,48). Moreover, illness-duration had an effect on the performance of patients in prosodic tasks (47).…”

Section: Production Of Non-emotional Prosody In Patients With Schizopmentioning

confidence: 99%

Speech Prosody as a Bridge Between Psychopathology and Linguistics: The Case of the Schizophrenia Spectrum

et al. 2020

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Patients with schizophrenia spectrum disorders experience severe difficulties in interpersonal communication, as described by traditional psychopathology and current research on social cognition. From a linguistic perspective, pragmatic abilities are crucial for successful communication. Empirical studies have shown that these abilities are significantly impaired in this group of patients. Prosody, the tone of voice with which words and sentences are pronounced, is one of the most important carriers of pragmatic meaning and can serve a range of functions from linguistic to emotional ones. Most of the existing literature on prosody of patients with schizophrenia spectrum disorders focuses on the expression of emotion, generally showing significant impairments. By contrast, the use of non-emotional prosody in these patients is scarcely investigated. In this paper, we first present a linguistic model to classify prosodic functions. Second, we discuss existing studies on the use of non-emotional prosody in these patients, providing an overview of the state of the art. Third, we delineate possible future lines of research in this field, also taking into account some classical psychopathological assumptions, for both diagnostic and therapeutic purposes.

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Associations of acoustically measured tongue/jaw movements and portion of time speaking with negative symptom severity in patients with schizophrenia in Italy and the United States

Cited by 26 publications

References 13 publications

Automated assessment of psychiatric disorders using speech: A systematic review

Automated assessment of psychiatric disorders using speech: A systematic review

Analyzing acoustic and prosodic fluctuations in free speech to predict psychosis onset in high-risk youths

Speech Prosody as a Bridge Between Psychopathology and Linguistics: The Case of the Schizophrenia Spectrum

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