Schizophrenia diagnosis support with spectral and cepstral features of speech

Teixeira, Felipe; Mendes, João; Soares,Salviano F. P.; Abreu, J. L. Pio; Teixeira, João Paulo

http://hdl.handle.net/10198/35211

Use this identifier to reference this record.

Name:	Description:	Size:	Format:
Schizophrenia Diagnosis.pdf		2.32 MB	Adobe PDF	Download

Send Feedback

Authors

Teixeira, Felipe

Mendes, João

Soares,Salviano F. P.

Abreu, J. L. Pio

Teixeira, João Paulo

Abstract(s)

Schizophrenia is a severe mental illness affecting over 20 million people worldwide, significantly impairing quality of life and daily functioning. Current diagnostic methods rely heavily on subjective assessments and interactions between doctors and patients, leaving room for potential misdiagnoses. Recent advancements in technology have introduced non-invasive, fast, and user-friendly approaches, such as machine learning, to support psychiatric diagnosis. In this study, spectral features extracted from speech samples of individuals with and without schizophrenia were analyzed. Using an ensemble bagged tree model, we achieved an accuracy of 96.3%, a sensitivity of 94.6%, and an F1-score of 95.4%. These results highlight the potential of speech-based machine learning models as effective tools for aiding schizophrenia diagnosis.

Keywords

Schizophrenia Speech features Machine Learning Ensemble bagged tree model

URI

http://hdl.handle.net/10198/35211

Citation

Teixeira, Felipe; Mendes, João; Soares,Salviano F. P.; Abreu, J. L. Pio; Teixeira, João Paulo (2026). Schizophrenia diagnosis support with spectral and cepstral features of speech. In Optimization, Learning Algorithms and Applications: 5th International Conference - Part 1. Cham: Springer Nature. p. 175–186. ISBN 9783032001368