Associations of Vocal Features, Psychiatric Symptoms, and Cognitive Functions in Schizophrenia

Tomoya Natsuyama,1 Enkhmurun Chibaatar,1 Yuko Shibata,2 Naomichi Okamoto,1 John Noel Cruz Victorino,2 Atsuko Ikenouchi,1 Tomohiro Shibata,2 Reiji Yoshimura1 1Department of Psychiatry, University of Occupational and Environmental Health, Kitakyushu, Fukuoka, 807-8555, Japan; 2Department of Life Science and System Engineering, Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu, Fukuoka, 808-0135, JapanCorrespondence: Reiji Yoshimura, Department of Psychiatry, University of Occupational and Environmental Health, 1-1, Iseigaoka, Yahatanishi, Kitakyushu, Fukuoka, 807-8555, Japan, Tel +81-93-691-7253, Email [email protected]: This study explored the use of advanced computational techniques in vocal analysis to improve the assessment of psychiatric symptoms and cognitive functions in schizophrenia. We hypothesized that digital signal processing techniques, such as mel spectrogram and mel-frequency cepstral coefficients (MFCC), could be used for objective evaluation of psychiatric symptoms and cognitive functions based on the analysis of alterations in the vocal characteristics.Patients and Methods: Voice samples from 14 participants diagnosed with schizophrenia (92.9% female) were collected using a microphone array, and vocal features were extracted from the samples using mel spectrogram and MFCC techniques. Psychiatric symptoms and cognitive functions were assessed using the Positive and Negative Syndrome Scale (PANSS) and the computer-based tool Cognitrax.Results: We found significant negative correlations between specific vocal features (mel spectrogram and MFCC) and cognitive functions, particularly working memory (β = − 0.645, p = 0.023) and sustained attention (β = − 0.626, p = 0.029). No direct correlations were found between vocal features and psychiatric symptoms, as measured by PANSS scores. However, the correlations between cognitive functions and PANSS total scores were significant (β = − 0.604, p = 0.037), suggesting that cognitive functions may mediate the relationship between psychiatric symptoms and vocal characteristics.Conclusion: This study underscores the potential of vocal analysis as a non-invasive tool for assessing cognitive impairment in schizophrenia. Future research should focus on expanding the sample size and including diverse populations to enhance the generalizability of these findings.Plain Language Summary: Why was the study done? Schizophrenia is a complex mental health disorder that affects how people think, feel, and behave, often leading to challenges in communication and cognitive function. This study aimed to explore how changes in vocal features can relate to the severity of psychiatric symptoms and cognitive abilities in individuals diagnosed with schizophrenia. We wanted to fill a gap in existing research about the connections between vocal patterns and cognitive functions in people with schizophrenia.What did the researchers do and find? We analyzed voice data from 14 participants to see if there were links between their vocal features, psychiatric symptoms, and cognitive functions. Our findings showed that poorer cognitive abilities, like memory and attentional control, negatively correlated with certain vocal characteristics. Additionally, higher psychiatric symptom severity was associated with worse cognitive performance.What do these results mean? This study suggests that analyzing vocal features could help us better understand cognitive impairments in schizophrenia, offering new ways to assess patients. By looking at how vocal features and cognitive functions relate, we can develop improved evaluation methods that may lead to better treatment outcomes. This research adds to our understanding of schizophrenia and highlights the importance of using vocal analysis in future studies to enhance clinical assessments and interventions.Keywords: cognition, schizophrenic psychology, diagnostic and statistical manual of mental disorders, positive and negative syndrome scale, acoustics, computer-assisted signal processing