Teager Energy Cepstral Coefficients For Classification of Dysarthric Speech Severity-Level

Dysarthria is a neuro-motor speech impairment that renders speech unintelligibility, which is generally imperceptible to humans w.r.t severity-levels. Dysarthric speech classification acts as a diagnostic tool for evaluating the advancement in a patient's severity condition and also aids in automatic dysarthric speech recognition systems (an important assistive speech technology). This study investigates the significance of Teager Energy Cepstral Coefficients (TECC) in dysarthric speech classification using three deep learning architectures, namely, Convolutional Neural Network (CNN), Light-CNN (LCNN), and Residual Networks (ResNet). The performance of TECC is compared with state-of-the-art features, such as Short-Time Fourier Transform (STFT), Mel Frequency Cepstral Coefficients (MFCC), and Linear Frequency Cepstral Coefficients (LFCC). In addition, this study also investigate the effectiveness of cepstral features over the spectral features for this problem. The highest classification accuracy achieved using UA-Speech corpus is 97.18%, 94.63%, and 98.02% (i.e., absolute improvement of 1.98%, 1.41%, and 1.69%) with CNN, LCNN, and ResNet, respectively, as compared to the MFCC. Further, we evaluate feature discriminative capability using $F1$-score, Matthew's Correlation Coefficient (MCC), Jaccard index, and Hamming loss. Finally, analysis of latency period w.r.t. state-of-the-art feature sets indicates the potential of TECC for practical deployment of the severity-level classification system.