TY - JOUR

T1 - Voice Patterns as Markers of Schizophrenia

T2 - Building a Cumulative Generalizable Approach Via a Cross-Linguistic and Meta-analysis Based Investigation

AU - Parola, Alberto

AU - Simonsen, Arndis

AU - Lin, Jessica Mary

AU - Zhou, Yuan

AU - Wang, Huiling

AU - Ubukata, Shiho

AU - Koelkebeck, Katja

AU - Bliksted, Vibeke

AU - Fusaroli, Riccardo

N1 - © The Author(s) 2023. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.

PY - 2023/3

Y1 - 2023/3

N2 - BACKGROUND AND HYPOTHESIS: Voice atypicalities are potential markers of clinical features of schizophrenia (eg, negative symptoms). A recent meta-analysis identified an acoustic profile associated with schizophrenia (reduced pitch variability and increased pauses), but also highlighted shortcomings in the field: small sample sizes, little attention to the heterogeneity of the disorder, and to generalizing findings to diverse samples and languages.STUDY DESIGN: We provide a critical cumulative approach to vocal atypicalities in schizophrenia, where we conceptually and statistically build on previous studies. We aim at identifying a cross-linguistically reliable acoustic profile of schizophrenia and assessing sources of heterogeneity (symptomatology, pharmacotherapy, clinical and social characteristics). We relied on previous meta-analysis to build and analyze a large cross-linguistic dataset of audio recordings of 231 patients with schizophrenia and 238 matched controls (>4000 recordings in Danish, German, Mandarin and Japanese). We used multilevel Bayesian modeling, contrasting meta-analytically informed and skeptical inferences.STUDY RESULTS: We found only a minimal generalizable acoustic profile of schizophrenia (reduced pitch variability), while duration atypicalities replicated only in some languages. We identified reliable associations between acoustic profile and individual differences in clinical ratings of negative symptoms, medication, age and gender. However, these associations vary across languages.CONCLUSIONS: The findings indicate that a strong cross-linguistically reliable acoustic profile of schizophrenia is unlikely. Rather, if we are to devise effective clinical applications able to target different ranges of patients, we need first to establish larger and more diverse cross-linguistic datasets, focus on individual differences, and build self-critical cumulative approaches.

AB - BACKGROUND AND HYPOTHESIS: Voice atypicalities are potential markers of clinical features of schizophrenia (eg, negative symptoms). A recent meta-analysis identified an acoustic profile associated with schizophrenia (reduced pitch variability and increased pauses), but also highlighted shortcomings in the field: small sample sizes, little attention to the heterogeneity of the disorder, and to generalizing findings to diverse samples and languages.STUDY DESIGN: We provide a critical cumulative approach to vocal atypicalities in schizophrenia, where we conceptually and statistically build on previous studies. We aim at identifying a cross-linguistically reliable acoustic profile of schizophrenia and assessing sources of heterogeneity (symptomatology, pharmacotherapy, clinical and social characteristics). We relied on previous meta-analysis to build and analyze a large cross-linguistic dataset of audio recordings of 231 patients with schizophrenia and 238 matched controls (>4000 recordings in Danish, German, Mandarin and Japanese). We used multilevel Bayesian modeling, contrasting meta-analytically informed and skeptical inferences.STUDY RESULTS: We found only a minimal generalizable acoustic profile of schizophrenia (reduced pitch variability), while duration atypicalities replicated only in some languages. We identified reliable associations between acoustic profile and individual differences in clinical ratings of negative symptoms, medication, age and gender. However, these associations vary across languages.CONCLUSIONS: The findings indicate that a strong cross-linguistically reliable acoustic profile of schizophrenia is unlikely. Rather, if we are to devise effective clinical applications able to target different ranges of patients, we need first to establish larger and more diverse cross-linguistic datasets, focus on individual differences, and build self-critical cumulative approaches.

KW - Bayes Theorem

KW - Humans

KW - Linguistics

KW - Schizophrenia/diagnosis

KW - Voice

KW - speech signal

KW - digital phenotyping

KW - negative symptoms

KW - vocal analysis

KW - prosody

KW - psychosis

U2 - 10.1093/schbul/sbac128

DO - 10.1093/schbul/sbac128

M3 - Journal article

C2 - 36946527

VL - 49

SP - S125-S141

JO - Schizophrenia Bulletin

JF - Schizophrenia Bulletin

SN - 0586-7614

IS - Suppl_2

ER -