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Lucarini V, Grice M, Wehrle S, Cangemi F, Giustozzi F, Amorosi S, Rasmi F, Fascendini N, Magnani F, Marchesi C, Scoriels L, Vogeley K, Krebs MO, Tonna M. Language in interaction: turn-taking patterns in conversations involving individuals with schizophrenia. Psychiatry Res 2024; 339:116102. [PMID: 39089189 DOI: 10.1016/j.psychres.2024.116102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 05/15/2024] [Accepted: 07/23/2024] [Indexed: 08/03/2024]
Abstract
Individuals with schizophrenia generally show difficulties in interpersonal communication. Linguistic analyses shed new light on speech atypicalities in schizophrenia. However, very little is known about conversational interaction management by these individuals. Moreover, the relationship between linguistic features, psychopathology, and patients' subjectivity has received limited attention to date. We used a novel methodology to explore dyadic conversations involving 58 participants (29 individuals with schizophrenia and 29 control persons) and medical doctors. High-quality stereo recordings were obtained and used to quantify turn-taking patterns. We investigated psychopathological dimensions and subjective experiences using the Positive and Negative Syndrome Scale for Schizophrenia (PANSS), the Examination of Anomalous Self Experience scale (EASE), the Autism Rating Scale (ARS) and the Abnormal Bodily Phenomena questionnaire (ABPq). Different turn-taking patterns of both patients and interviewers characterised conversations involving individuals with schizophrenia. We observed higher levels of overlap and mutual silence in dialogues with the patients compared to dialogues with control persons. Mutual silence was associated with negative symptom severity; no dialogical feature was correlated with anomalous subjective experiences. Our findings suggest that individuals with schizophrenia display peculiar turn-taking behaviour, thereby enhancing our understanding of interactional coordination in schizophrenia.
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Affiliation(s)
- Valeria Lucarini
- Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, Team: Pathophysiology of psychiatric disorders: development and vulnerability, Paris 75014, France; GHU Paris Psychiatrie et Neurosciences, CJAAD, Evaluation, Prevention and Therapeutic Innovation Department, Hôpital Sainte Anne, Paris 75014, France; CNRS GDR 3557-Institut de Psychiatrie, France.
| | - Martine Grice
- IfL-Phonetics, University of Cologne, Cologne, Germany
| | - Simon Wehrle
- IfL-Phonetics, University of Cologne, Cologne, Germany
| | | | - Francesca Giustozzi
- Psychiatric Unit, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Stefano Amorosi
- Psychiatric Unit, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Francesco Rasmi
- Psychiatric Unit, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Nikolas Fascendini
- Psychiatric Unit, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Francesca Magnani
- Psychiatric Unit, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Carlo Marchesi
- Psychiatric Unit, Department of Medicine and Surgery, University of Parma, Parma, Italy; Department of Mental Health, Local Health Service, Parma, Italy
| | - Linda Scoriels
- GHU Paris Psychiatrie et Neurosciences, CJAAD, Evaluation, Prevention and Therapeutic Innovation Department, Hôpital Sainte Anne, Paris 75014, France
| | - Kai Vogeley
- Department of Psychiatry and Psychotherapy, Medical Faculty, University of Cologne, Cologne, Germany; Cognitive Neuroscience (INM-3), Institute of Neuroscience and Medicine, Research Center Jülich, Jülich, Germany
| | - Marie-Odile Krebs
- Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, Team: Pathophysiology of psychiatric disorders: development and vulnerability, Paris 75014, France; GHU Paris Psychiatrie et Neurosciences, CJAAD, Evaluation, Prevention and Therapeutic Innovation Department, Hôpital Sainte Anne, Paris 75014, France; CNRS GDR 3557-Institut de Psychiatrie, France
| | - Matteo Tonna
- Psychiatric Unit, Department of Medicine and Surgery, University of Parma, Parma, Italy; Department of Mental Health, Local Health Service, Parma, Italy
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Tracey B, Volfson D, Glass J, Haulcy R, Kostrzebski M, Adams J, Kangarloo T, Brodtmann A, Dorsey ER, Vogel A. Towards interpretable speech biomarkers: exploring MFCCs. Sci Rep 2023; 13:22787. [PMID: 38123603 PMCID: PMC10733367 DOI: 10.1038/s41598-023-49352-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 12/07/2023] [Indexed: 12/23/2023] Open
Abstract
While speech biomarkers of disease have attracted increased interest in recent years, a challenge is that features derived from signal processing or machine learning approaches may lack clinical interpretability. As an example, Mel frequency cepstral coefficients (MFCCs) have been identified in several studies as a useful marker of disease, but are regarded as uninterpretable. Here we explore correlations between MFCC coefficients and more interpretable speech biomarkers. In particular we quantify the MFCC2 endpoint, which can be interpreted as a weighted ratio of low- to high-frequency energy, a concept which has been previously linked to disease-induced voice changes. By exploring MFCC2 in several datasets, we show how its sensitivity to disease can be increased by adjusting computation parameters.
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Affiliation(s)
- Brian Tracey
- Takeda Pharamaceuticals, Data Science Institute, Cambridge, MA, 02142, USA.
| | - Dmitri Volfson
- Takeda Pharamaceuticals, Data Science Institute, Cambridge, MA, 02142, USA
| | - James Glass
- Massachusetts Institute of Technology, CSAIL, Cambridge, MA, 02139, USA
| | - R'mani Haulcy
- Massachusetts Institute of Technology, CSAIL, Cambridge, MA, 02139, USA
| | - Melissa Kostrzebski
- Center for Health + Technology (CHeT), University of Rochester Medical Center, Rochester, NY, USA
| | - Jamie Adams
- Center for Health + Technology (CHeT), University of Rochester Medical Center, Rochester, NY, USA
| | - Tairmae Kangarloo
- Takeda Pharamaceuticals, Data Science Institute, Cambridge, MA, 02142, USA
| | - Amy Brodtmann
- Monash University, Melbourne, VIC, Australia
- University of Melbourne, Parkville, VIC, 3010, Australia
| | - E Ray Dorsey
- Center for Health + Technology (CHeT), University of Rochester Medical Center, Rochester, NY, USA
| | - Adam Vogel
- University of Melbourne, Parkville, VIC, 3010, Australia
- Redenlab Inc, Melbourne, VIC, 3010, Australia
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Geng P, Fan N, Ling R, Li Z, Guo H, Lu Q, Chen X. Acoustic Characteristics of Mandarin Speech in Male Drug Users. J Voice 2023:S0892-1997(23)00269-2. [PMID: 37827893 DOI: 10.1016/j.jvoice.2023.08.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 08/21/2023] [Accepted: 08/22/2023] [Indexed: 10/14/2023]
Abstract
AIM Drug use/addiction has a profound impact on the physical and mental health of individuals. Previous studies have indicated that drug users may experience speech perception disorders, including speech illusion and challenges in recognizing emotional speech. However, the influence of drugs on speech production, as another crucial aspect of speech communication, has not been thoroughly examined. Therefore, the current study aimed to investigate how drugs affect the acoustic characteristics of speech in Chinese male drug users. METHOD Speech recordings were collected from a total of 160 male drug users (including 106 heroin users, 23 ketamine users, and 31 methamphetamine users) and 55 male healthy controls with no history of drug use. Acoustic analysis was conducted on the collected speech data from these groups, and classification analysis was performed using five supervised learning algorithms. RESULTS The results demonstrated that drug users exhibited smaller F0 standard deviation, reduced loudness, cepstral peak prominence, and formant relative energies, as well as higher H1-A3, longer unvoiced segments, and fewer voiced segments per second compared to the control group. The classification analyses yielded good performance in classifying drug users and non-drug users, with an accuracy above 86%. Moreover, the identification of the three groups of drug users achieved an accuracy of approximately 70%. Additionally, the study revealed different effects on speech production among the three types of drugs. CONCLUSION The above findings indicate the presence of speech disorders, such as vocal hoarseness, in drug users, thus confirming the assumption that the acoustic characteristics of speech in drug users deviates from the norm. This study not only fills the knowledge gap regarding the effects of drugs on the speech production of Chinese male drug users but also provides a more comprehensive understanding of how drugs impact human behaviors. Furthermore, this research provides theoretical foundations of detoxification and speech rehabilitation for drug users.
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Affiliation(s)
- Puyang Geng
- Department of Audio, Video, and Electronic Forensics, Academy of Forensic Science, Shanghai, China; Shanghai Forensic Service Platform, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai, China.
| | - Ningxue Fan
- Information Security and Social Management Innovation Lab, Shanghai Open University, Shanghai, China
| | - Rong Ling
- Department of Audio, Video, and Electronic Forensics, Academy of Forensic Science, Shanghai, China; Shanghai Forensic Service Platform, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai, China
| | - Zhijun Li
- Department of Audio, Video, and Electronic Forensics, Academy of Forensic Science, Shanghai, China; Shanghai Forensic Service Platform, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai, China
| | - Hong Guo
- Department of Audio, Video, and Electronic Forensics, Academy of Forensic Science, Shanghai, China; Shanghai Forensic Service Platform, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai, China
| | - Qimeng Lu
- Department of Audio, Video, and Electronic Forensics, Academy of Forensic Science, Shanghai, China; Shanghai Forensic Service Platform, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai, China
| | - Xingwen Chen
- Network Security Team, Public Security Department of Guangxi Province, Nanning, Guangxi, China
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