Hearing vocals to recognize schizophrenia: speech discriminant analysis with fusion of emotions and features based on deep learning.

Background and objective: Accurate 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.

Methods: A 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.

Results: The 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.

Conclusions: Speech 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.