Deep neural network architectures for dysarthric speech analysis and recognition

Abstract

This paper investigates the ability of deep neural networks (DNNs) to improve the automatic recognition of dysarthric speech through the use of convolutional neural networks (CNNs) and long short-term memory (LSTM) neural networks. Dysarthria is one of the most common speech communication disorders associated with neurological impairments that can drastically reduce the intelligibility of speech. The aim of the present study is twofold. First, it compares three different input features for training and testing dysarthric speech recognition systems. These features are the mel-frequency cepstral coefficients (MFCCs), mel-frequency spectral coefficients (MFSCs), and the perceptual linear prediction features (PLPs). Second, the performance of the CNN- and LSTM-based architectures is compared against a state-of-the-art baseline system based on hidden Markov models (HMMs) and Gaussian mixture models (GMMs) to determine the best dysarthric speech recognizer. Experimental results show that the CNN-based system using perceptual linear prediction features provides a recognition rate that can reach 82%, which constitutes relative improvement of 11% and 32% when compared to the performance of LSTM- and GMM-HMM-based systems, respectively.