Data Augmentation using Healthy Speech for Dysarthric Speech Recognition

Dysarthria refers to a speech disorder caused by trauma to the brain areas concerned with motor aspects of speech giving rise to effortful, slow, slurred or prosodically abnormal speech. Traditional Automatic Speech Recognizers (ASR) perform poorly on dysarthric speech recognition tasks, owing mostly to insufficient dysarthric speech data. Speaker related challenges complicates data collection process for dysarthric speech. In this paper, we explore data augmentation using temporal and speed modifications to healthy speech to simulate dysarthric speech. DNN-HMM based Automatic Speech Recognition (ASR) and Random Forest based classification were used for evaluation of the proposed method. Dysarthric speech, generated synthetically, is classified for severity level using a Random Forest classifier that is trained on actual dysarthric speech. ASR trained on healthy speech, augmented with simulated dysarthric speech is evaluated for dysarthric speech recognition. All evaluations were carried out using Universal Access dysarthric speech corpus. An absolute improvement of 4.24% and 2% WAS achieved using tempo based and speed based data augmentation respectively as compared to ASR performance using healthy speech alone for training.