Non-verbal speech cues as objective measures for negative symptoms in patients with schizophrenia
Negative symptoms in schizophrenia are associated with significant burden and possess little to no robust treatments in clinical practice today. One key obstacle impeding the development of better treatment methods is the lack of an objective measure. Since negative symptoms almost always adversely...
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creator | Tahir, Yasir Yang, Zixu Chakraborty, Debsubhra Thalmann, Nadia Thalmann, Daniel Maniam, Yogeswary Binte Abdul Rashid, Nur Amirah Tan, Bhing-Leet Lee Chee Keong, Jimmy Dauwels, Justin |
description | Negative symptoms in schizophrenia are associated with significant burden and possess little to no robust treatments in clinical practice today. One key obstacle impeding the development of better treatment methods is the lack of an objective measure. Since negative symptoms almost always adversely affect speech production in patients, speech dysfunction have been considered as a viable objective measure. However, researchers have mostly focused on the verbal aspects of speech, with scant attention to the non-verbal cues in speech. In this paper, we have explored non-verbal speech cues as objective measures of negative symptoms of schizophrenia. We collected an interview corpus of 54 subjects with schizophrenia and 26 healthy controls. In order to validate the non-verbal speech cues, we computed the correlation between these cues and the NSA-16 ratings assigned by expert clinicians. Significant correlations were obtained between these non-verbal speech cues and certain NSA indicators. For instance, the correlation between Turn Duration and Restricted Speech is -0.5, Response time and NSA Communication is 0.4, therefore indicating that poor communication is reflected in the objective measures, thus validating our claims. Moreover, certain NSA indices can be classified into observable and non-observable classes from the non-verbal speech cues by means of supervised classification methods. In particular the accuracy for Restricted speech quantity and Prolonged response time are 80% and 70% respectively. We were also able to classify healthy and patients using non-verbal speech features with 81.3% accuracy. |
doi_str_mv | 10.1371/journal.pone.0214314 |
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One key obstacle impeding the development of better treatment methods is the lack of an objective measure. Since negative symptoms almost always adversely affect speech production in patients, speech dysfunction have been considered as a viable objective measure. However, researchers have mostly focused on the verbal aspects of speech, with scant attention to the non-verbal cues in speech. In this paper, we have explored non-verbal speech cues as objective measures of negative symptoms of schizophrenia. We collected an interview corpus of 54 subjects with schizophrenia and 26 healthy controls. In order to validate the non-verbal speech cues, we computed the correlation between these cues and the NSA-16 ratings assigned by expert clinicians. Significant correlations were obtained between these non-verbal speech cues and certain NSA indicators. For instance, the correlation between Turn Duration and Restricted Speech is -0.5, Response time and NSA Communication is 0.4, therefore indicating that poor communication is reflected in the objective measures, thus validating our claims. Moreover, certain NSA indices can be classified into observable and non-observable classes from the non-verbal speech cues by means of supervised classification methods. In particular the accuracy for Restricted speech quantity and Prolonged response time are 80% and 70% respectively. We were also able to classify healthy and patients using non-verbal speech features with 81.3% accuracy.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0214314</identifier><identifier>PMID: 30964869</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Accuracy ; Adult ; Analysis ; Artificial intelligence ; Automation ; Biology and Life Sciences ; Clinical medicine ; Communication ; Computer and Information Sciences ; Correlation ; Cues ; Diagnosis ; Emotional behavior ; Engineering and Technology ; Female ; Humans ; International conferences ; Male ; Medicine and Health Sciences ; Mental disorders ; Mental health care ; Natural language processing ; Nonverbal communication ; Patients ; People and Places ; Physical Sciences ; Psychiatry ; Psychological aspects ; Researchers ; Response time ; Schizophrenia ; Schizophrenia - physiopathology ; Signal processing ; Signs and symptoms ; Social Sciences ; Speech ; Speech - physiology ; Surveys and Questionnaires</subject><ispartof>PloS one, 2019-04, Vol.14 (4), p.e0214314-e0214314</ispartof><rights>COPYRIGHT 2019 Public Library of Science</rights><rights>2019 Tahir et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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For instance, the correlation between Turn Duration and Restricted Speech is -0.5, Response time and NSA Communication is 0.4, therefore indicating that poor communication is reflected in the objective measures, thus validating our claims. Moreover, certain NSA indices can be classified into observable and non-observable classes from the non-verbal speech cues by means of supervised classification methods. In particular the accuracy for Restricted speech quantity and Prolonged response time are 80% and 70% respectively. We were also able to classify healthy and patients using non-verbal speech features with 81.3% accuracy.</description><subject>Accuracy</subject><subject>Adult</subject><subject>Analysis</subject><subject>Artificial intelligence</subject><subject>Automation</subject><subject>Biology and Life Sciences</subject><subject>Clinical medicine</subject><subject>Communication</subject><subject>Computer and Information Sciences</subject><subject>Correlation</subject><subject>Cues</subject><subject>Diagnosis</subject><subject>Emotional behavior</subject><subject>Engineering and Technology</subject><subject>Female</subject><subject>Humans</subject><subject>International conferences</subject><subject>Male</subject><subject>Medicine and Health Sciences</subject><subject>Mental disorders</subject><subject>Mental health care</subject><subject>Natural language processing</subject><subject>Nonverbal communication</subject><subject>Patients</subject><subject>People and Places</subject><subject>Physical 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and possess little to no robust treatments in clinical practice today. One key obstacle impeding the development of better treatment methods is the lack of an objective measure. Since negative symptoms almost always adversely affect speech production in patients, speech dysfunction have been considered as a viable objective measure. However, researchers have mostly focused on the verbal aspects of speech, with scant attention to the non-verbal cues in speech. In this paper, we have explored non-verbal speech cues as objective measures of negative symptoms of schizophrenia. We collected an interview corpus of 54 subjects with schizophrenia and 26 healthy controls. In order to validate the non-verbal speech cues, we computed the correlation between these cues and the NSA-16 ratings assigned by expert clinicians. Significant correlations were obtained between these non-verbal speech cues and certain NSA indicators. For instance, the correlation between Turn Duration and Restricted Speech is -0.5, Response time and NSA Communication is 0.4, therefore indicating that poor communication is reflected in the objective measures, thus validating our claims. Moreover, certain NSA indices can be classified into observable and non-observable classes from the non-verbal speech cues by means of supervised classification methods. In particular the accuracy for Restricted speech quantity and Prolonged response time are 80% and 70% respectively. We were also able to classify healthy and patients using non-verbal speech features with 81.3% accuracy.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>30964869</pmid><doi>10.1371/journal.pone.0214314</doi><tpages>e0214314</tpages><orcidid>https://orcid.org/0000-0003-0406-3992</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Accuracy Adult Analysis Artificial intelligence Automation Biology and Life Sciences Clinical medicine Communication Computer and Information Sciences Correlation Cues Diagnosis Emotional behavior Engineering and Technology Female Humans International conferences Male Medicine and Health Sciences Mental disorders Mental health care Natural language processing Nonverbal communication Patients People and Places Physical Sciences Psychiatry Psychological aspects Researchers Response time Schizophrenia Schizophrenia - physiopathology Signal processing Signs and symptoms Social Sciences Speech Speech - physiology Surveys and Questionnaires |
title | Non-verbal speech cues as objective measures for negative symptoms in patients with schizophrenia |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-13T00%3A09%3A09IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Non-verbal%20speech%20cues%20as%20objective%20measures%20for%20negative%20symptoms%20in%20patients%20with%20schizophrenia&rft.jtitle=PloS%20one&rft.au=Tahir,%20Yasir&rft.date=2019-04-09&rft.volume=14&rft.issue=4&rft.spage=e0214314&rft.epage=e0214314&rft.pages=e0214314-e0214314&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0214314&rft_dat=%3Cgale_plos_%3EA581813755%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2208024670&rft_id=info:pmid/30964869&rft_galeid=A581813755&rft_doaj_id=oai_doaj_org_article_5fa0b7a894ae434cb88b619533f2c119&rfr_iscdi=true |