ArticleBMC psychiatry2025
Hearing vocals to recognize schizophrenia: speech discriminant analysis with fusion of emotions and features based on deep learning.
Article in BMC psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Speech-based computational approaches for classification and symptom monitoring in schizophrenia spectrum disorders: a systematic review and meta-analysis.BMC psychiatry · 2026Pooled it
- Taking a look at your speech: identifying diagnostic status and negative symptoms of psychosis using convolutional neural networks.NPP - digital psychiatry and neuroscience · 2025Article
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10 authors.
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Abstract
BACKGROUND AND
objectiveAccurate detection of schizophrenia poses a grand challenge as a complex and heterogeneous mental disorder. Current diagnostic criteria rely primarily on clinical symptoms, which may not fully capture individual differences and the heterogeneity of the disorder. In this study, a discriminative model of schizophrenic speech based on deep learning is developed, which combines different emotional stimuli and features.
methodsA total of 156 schizophrenia patients and 74 healthy controls participated in the study, reading three fixed texts with varying emotional stimuli. The log-Mel spectrogram and Mel-frequency cepstral coefficients (MFCCs) were extracted using the librosa-0.9.2 toolkit. Convolutional neural networks were applied to analyze the log-Mel spectrogram. The effects of different emotional stimuli and the fusion of demographic information and MFCCs on schizophrenia detection were examined.
resultsThe discriminant analysis results showed superior performance for neutral emotional stimuli compared to positive and negative stimuli. Integrating different emotional stimuli and fusing features with personal information improved sensitivity and specificity. The best discriminant model achieved an accuracy of 91.7%, sensitivity of 94.9%, specificity of 85.1%, and ROC-AUC of 0.963.
conclusionsSpeech analysis under neutral emotional stimulation demonstrated greater differences between schizophrenia patients and healthy controls, enhancing discriminative analysis of schizophrenia. Integrating different emotions, demographic information and MFCCs improved the accuracy of schizophrenia detection. This study provides a methodological foundation for constructing a personalized speech detection model for schizophrenia.
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