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- A narrative review of speech and EEG features for schizophrenia detection: progress and challengesPublication . Teixeira, Felipe; Costa, Miguel Rocha; Abreu, J.L. Pio; Cabral, Manuel; Soares, Salviano; Teixeira, João PauloSchizophrenia is a mental illness that affects an estimated 21 million people worldwide. The literature establishes that electroencephalography (EEG) is a well-implemented means of studying and diagnosing mental disorders. However, it is known that speech and language provide unique and essential information about human thought. Semantic and emotional content, semantic coherence, syntactic structure, and complexity can thus be combined in a machine learning process to detect schizophrenia. Several studies show that early identification is crucial to prevent the onset of illness or mitigate possible complications. Therefore, it is necessary to identify disease-specific biomarkers for an early diagnosis support system. This work contributes to improving our knowledge about schizophrenia and the features that can identify this mental illness via speech and EEG. The emotional state is a specific characteristic of schizophrenia that can be identified with speech emotion analysis. The most used features of speech found in the literature review are fundamental frequency (F0), intensity/loudness (I), frequency formants (F1, F2, and F3), Mel-frequency cepstral coefficients (MFCC's), the duration of pauses and sentences (SD), and the duration of silence between words. Combining at least two feature categories achieved high accuracy in the schizophrenia classification. Prosodic and spectral or temporal features achieved the highest accuracy. The work with higher accuracy used the prosodic and spectral features QEVA, SDVV, and SSDL, which were derived from the F0 and spectrogram. The emotional state can be identified with most of the features previously mentioned (F0, I, F1, F2, F3, MFCCs, and SD), linear prediction cepstral coefficients (LPCC), linear spectral features (LSF), and the pause rate. Using the event-related potentials (ERP), the most promissory features found in the literature are mismatch negativity (MMN), P2, P3, P50, N1, and N2. The EEG features with higher accuracy in schizophrenia classification subjects are the nonlinear features, such as Cx, HFD, and Lya.
- F0, LPC, and MFCC analysis for emotion recognition based on speechPublication . Teixeira, Felipe; Teixeira, João Paulo; Soares, Salviano; Abreu, J.L. PioIn this work, research was done to understand what is needed to build a database to recognise emotions through speech. Some features that can highlight a good success rate for emotion recognition through speech were investigated. Also studied were some characteristics (symptoms) that can be associated with a specific emotional state. On the other hand, we also studied some features that can be used to identify some emotional states. A System Emotion Recognition (SER) was built with SVM, and the binary analysis was compared with a multi-category analysis. The binary analysis achieved an accuracy of 87.5% and the multi-class 42.6%. The parameters Fundamental Frequency-F0, Linear Predictive Coefficients (LPC), and Mel Frequency Cepstral Coeficients (MFCC) were used. The modest accuracy of this work was achieved using only F0, LPC and MFCC features.