Representation Learning Based Speech Assistive System for Persons With Dysarthria

An assistive system for persons with vocal impairment due to dysarthria converts dysarthric speech to normal speech or text. Because of the articulatory deficits, dysarthric speech recognition needs a robust learning technique. Representation learning is significant for complex tasks such as dysarthric speech recognition. We focus on robust representation for dysarthric speech recognition that involves recognizing sequential patterns of varying length utterances. We propose a hybrid framework that uses a generative learning based data representation with a discriminative learning based classifier. In this hybrid framework, we propose to use Example Specific Hidden Markov Models (ESHMMs) to obtain log-likelihood scores for a dysarthric speech utterance to form fixed dimensional score vector representation. This representation is used as an input to discriminative classifier such as support vector machine. The performance of the proposed approach is evaluated using UA-Speech database.The recognition accuracy is much better than the conventional hidden Markov model based approach and Deep Neural Network-Hidden Markov Model (DNN-HMM). The efficiency of the discriminative nature of score vector representation is proved for “very low” intelligibility words.