On improving acoustic models for TORGO dysarthric speech database

Assistive technologies based on speech have been shown to improve the quality of life of people affected with dysarthria, a motor speech disorder. Multiple ways to improve Gaussian mixture model-hidden Markov model (GMM-HMM) and deep neural network (DNN) based automatic speech recognition (ASR) systems for TORGO database for dysarthric speech are explored in this paper. Past attempts in developing ASR systems for TORGO database were limited to training just monophone models and doing speaker adaptation over them. Although a recent work attempted training triphone and neural network models, parameters like the number of context dependent states, dimensionality of the principal component features etc were not properly tuned. This paper develops speakerspecific ASR models for each dysarthric speaker in TORGO database by tuning parameters of GMM-HMM model, number of layers and hidden nodes in DNN. Employing dropout scheme and sequence discriminative training in DNN also gave significant gains. Speaker adapted features like feature-space maximum likelihood linear regression (FMLLR) are used to pass the speaker information to DNNs. To the best of our knowledge, this paper presents the best recognition accuracies for TORGO database till date.