Dysarthric Severity Categorization Based on Speech Intelligibility: A Hybrid Approach

Abstract

The intelligibility of speech is a primary component to assess the severity level of Dysarthria, a speech disorder, which is caused not only due to weakness in vocal motor muscles but also difficulty in controlling its movements. Prior information about the severity of Dysarthria, aids the therapist during the rehabilitation process. This paper introduces a novel hybrid architecture comprising Gaussian Mixture Model and Neural Network (GMM-NN) for categorizing Dysarthric severity into four classes based on speech intelligibility. Mel Frequency Cepstral Coefficients (MFCC) extracted from the segmented speech signals are used to train the classifier. The proposed model produced a 1.9% improvement in accuracy when compared to the baseline Gaussian Mixture Model (GMM). The Gaussian Mixture Model Deep Neural Network (GMM-DNN) and Gaussian Mixture Model Feed Forward Neural Network (GMM-FFNN) architectures showed an accuracy of 96.7% and 96.42% with F1 scores of 0.9649, 0.9604 respectively.