Continuous Wavelet Transform for Severity-Level Classification of Dysarthria

Abstract

Dysarthria is a neuro-motor speech defect that causes speech to be unintelligible and is largely unnoticeable to humans at various severity-levels. Dysarthric speech classification is used as a diagnostic method to assess the progression of a patient’s severity of the condition, as well as to aid with automatic dysarthric speech recognition systems (an important assistive speech technology). This study investigates the significance of Generalized Morse Wavelet (GMW)-based scalogram features for capturing the discriminative acoustic cues of dysarthric severity-level classification for low-frequency regions, using Convolutional Neural Network (CNN). The performance of scalogram-based features is compared with Short-Time Fourier Transform (STFT)-based features, and Mel spectrogram-based features. Compared to the STFT-based baseline features with a classification accuracy of 91.76%, the proposed Continuous Wavelet Transform (CWT)-based scalogram features achieve significantly improved classification accuracy of 95.17% on standard and statistically meaningful UA-Speech corpus. The remarkably improved results signify that for better dysarthric severity-level classification, the information in the low-frequency regions is more discriminative, as the proposed CWT-based time-frequency representation (scalogram) has a high-frequency resolution in the lower frequencies. On the other hand, STFT-based representations have constant resolution across all the frequency bands and therefore, are not as better suited for dysarthric severity-level classification, as the proposed Morse wavelet-based CWT features. In addition, we also perform experiments on the Mel spectrogram to demonstrate that even though the Mel spectrogram also has a high frequency resolution in the lower frequencies with a classification accuracy of 92.65%, the proposed system is better suited. We see an increase of 3.41% and 2.52% in classification accuracy of the proposed system to STFT and Mel spectrogram respectively. To that effect, the performance of the STFT, Mel spectrogram, and scalogram are analyzed using F1-Score, Matthew’s Correlation Coefficients (MCC), Jaccard Index, Hamming Loss, and Linear Discriminant Analysis (LDA) scatter plots.