Improved acoustic modeling for automatic dysarthric speech recognition

Abstract

Dysarthria is a neuromuscular disorder, occurs due to improper coordination of speech musculature. In order to improve the quality of life of people with speech disorder, assistive technology using automatic speech recognition (ASR) systems are gaining importance. Since it is difficult for dysarthric speakers to provide sufficient data, data insufficiency is one of the major problems in building an efficient dysarthric ASR system. In this paper, we focus on handling this issue by pooling data from unimpaired speech database. Then feature space maximum likelihood linear regression (fMLLR) transformation is applied on pooled data and dysarthric data to normalize the effect of inter-speaker variability. The acoustic model built using the combined features (acoustically transformed dysarthric + pooled features) gives an relative improvement of 18.09% and 50.00% over baseline system for Nemours database and Universal Access speech (digit set) database.