Analysis on speech signal features of manic patients

Zhang, Jing; Pan, Zhongde; Gui, Chao; Xue, Ting; Lin, Yezhe; Zhu, Jie; Cui, Donghong

doi:10.1016/j.jpsychires.2017.12.012

Cited by 33 publications

(20 citation statements)

References 23 publications

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“…Significant increases in tonality were observed including increases in median f0, and mean F1 and F2 . Furthermore, a significantly higher number of longer pauses were observed in depressive states than in euthymic or hypomanic states, and speech pauses became longer as patients transitioned into depressive states .…”

Section: Discussionmentioning

confidence: 92%

“…Significant increases in tonality were observed including increases in median f0, 81,82 and mean F1 and F2. 83 Furthermore, a significantly higher number of longer pauses were observed in depressive states than in euthymic or hypomanic states, 81 and speech pauses became longer as patients transitioned into depressive states. 13 Changes in speech in depression and mania are well known clinically as they are captured by the psychomotor retardation item in the Hamilton Depression Rating 84 and the speech rate item on the Young Mania Rating Scale 85 ; however, speech rate was not identified in the reviewed studies, and therefore, remains promising for future studies.…”

Section: Bipolar Disordermentioning

confidence: 97%

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Automated assessment of psychiatric disorders using speech: A systematic review

Low

Bentley

Ghosh

2020

Laryngoscope Investig Oto

318

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: 92%

Section: Bipolar Disordermentioning

confidence: 97%

Automated assessment of psychiatric disorders using speech: A systematic review

Low

Bentley

Ghosh

2020

Laryngoscope Investig Oto

318

268

View full text Add to dashboard Cite

show abstract

“…In depressive episodes, intonation has been typically described as flat, speech rate is slower than usual, and voice intensity and frequency are decreased [ 16 ]. Conversely, fewer studies have been conducted in mania, but faster than usual speech rate (i.e., pressure of speech), and increased voice volume are among the most commonly reported symptoms in mania [ 17 ], and have been hypothesized to be related to hyperarousal [ 14 , 17 ]. In mixed episodes, a study from 1938 highlighted that, in one patient experiencing a mixed episode, pressure of speech was associated with vigorous articulatory movements, wide pitch range, fast speech tempo, and infrequent prosodic pauses [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Vocal features obtained through automated methods in verbal fluency tasks can aid the identification of mixed episodes in bipolar disorder

et al. 2021

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There is a lack of consensus on the diagnostic thresholds that could improve the detection accuracy of bipolar mixed episodes in clinical settings. Some studies have shown that voice features could be reliable biomarkers of manic and depressive episodes compared to euthymic states, but none thus far have investigated whether they could aid the distinction between mixed and non-mixed acute bipolar episodes. Here we investigated whether vocal features acquired via verbal fluency tasks could accurately classify mixed states in bipolar disorder using machine learning methods. Fifty-six patients with bipolar disorder were recruited during an acute episode (19 hypomanic, 8 mixed hypomanic, 17 with mixed depression, 12 with depression). Nine different trials belonging to four conditions of verbal fluency tasks—letter, semantic, free word generation, and associational fluency—were administered. Spectral and prosodic features in three conditions were selected for the classification algorithm. Using the leave-one-subject-out (LOSO) strategy to train the classifier, we calculated the accuracy rate, the F1 score, and the Matthews correlation coefficient (MCC). For depression versus mixed depression, the accuracy and F1 scores were high, i.e., respectively 0.83 and 0.86, and the MCC was of 0.64. For hypomania versus mixed hypomania, accuracy and F1 scores were also high, i.e., 0.86 and 0.75, respectively, and the MCC was of 0.57. Given the high rates of correctly classified subjects, vocal features quickly acquired via verbal fluency tasks seem to be reliable biomarkers that could be easily implemented in clinical settings to improve diagnostic accuracy.

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“…Bipolar disorder. Significant increases in tonality were observed including increases in median f0 78,79 , and mean F1 and F2 80 . Furthermore, a significantly higher number of longer pauses were observed in depressive states than in euthymic or hypomanic states 78 , and speech pauses became longer as patients transitioned into depressive states 13 .…”

Section: Linking Acoustic Features To Symptoms Of Psychiatric Disordersmentioning

confidence: 93%

Automated Assessment of Psychiatric Disorders Using Speech: A Systematic Review

Low¹,

Bentley²,

Ghosh³

2019

Preprint

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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 disorder 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 datasets, 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.

show abstract

Analysis on speech signal features of manic patients

Cited by 33 publications

References 23 publications

Automated assessment of psychiatric disorders using speech: A systematic review

Automated assessment of psychiatric disorders using speech: A systematic review

Vocal features obtained through automated methods in verbal fluency tasks can aid the identification of mixed episodes in bipolar disorder

Automated Assessment of Psychiatric Disorders Using Speech: A Systematic Review

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