Automated Dysarthria Severity Classification Using Deep Learning Frameworks

Abstract

Dysarthria is a neuro-motor speech disorder that renders speech unintelligible, in proportional to its severity. Assessing the severity level of dysarthria, apart from being a diagnostic step to evaluate the patient's improvement, is also capable of aiding automatic dysarthric speech recognition systems. In this paper, a detailed study on dysarthia severity classification using various deep learning architectural choices, namely deep neural network (DNN), convolutional neural network (CNN) and long short-term memory network (LSTM) is carried out. Mel frequency cepstral coefficients (MFCCs) and its derivatives are used as features. Performance of these models are compared with a baseline support vector machine (SVM) classifier using the UA-Speech corpus and the TORGO database. The highest classification accuracy of 96.18% and 93.24% are reported for TORGO and UA-Speech respectively. Detailed analysis on performance of these models shows that a proper choice of a deep learning architecture can ensure better performance than the conventionally used SVM classifier.