Abstract
Pronunciation variation is a major problem in disordered speech recognition. This paper focus on handling the pronunciation variations in dysarthric speech by forming speaker-specific lexicons. A novel approach is proposed for identifying mispronunciations made by each dysarthric speaker, using state-specific vector (SSV) of phone-cluster adaptive training (Phone-CAT) acoustic model. SSV is low-dimensional vector estimated for each tied-state where each element in a vector denotes the weight of a particular monophone. The SSV indicates the pronounced phone using its dominant weight. This property of SSV is exploited in adapting the pronunciation of a particular dysarthric speaker using speaker-specific lexicons. Experimental validation on Nemours database showed an average relative improvement of 9% across all the speakers compared to the system built with canonical lexicon. Index Terms: Dysarthric speech recognition, phone-CAT, lexical modeling, pronunciations, phone confusion matrix
Highlights
Clinical applications of speech technology play an important role in aiding communication for people with motor speech disorders
Two different experiments were performed to compare with the baseline continuous density hidden Markov model (CDHMM) (Base) system
This paper focuses on improving the performance of dysarthric speech recognition systems by handling pronunciation errors
Summary
Clinical applications of speech technology play an important role in aiding communication for people with motor speech disorders. One such motor speech disorder is dysarthria, acquired secondary to stroke, traumatic brain injury, cerebral palsy etc. Some of the common characteristics of dysarthria include slurred speech, swallowing difficulty, slow speaking rate with increased effort to speak and muscle fatigue while speaking [1, 2]. All these effects affect the speech intelligibility and the social interaction ability of people with speech disorders. Acoustic models are usually built-in speaker adaptation framework [3, 4, 5]
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