Assessment of Dysarthria Using One-Word Speech Recognition with Hidden Markov Models

ABSTRACT

BACKGROUND: The gold standard in dysarthria assessment involves subjective analysis by a speech–language pathologist (SLP). We aimed to investigate the feasibility of dysarthria assessment using automatic speech recognition. METHODS: We developed an automatic speech recognition based software to assess dysarthria severity using hidden Markov models (HMMs). Word-specific HMMs were trained using the utterances from one hundred healthy individuals. Twenty-eight patients with dysarthria caused by neurological disorders, including stroke, traumatic brain injury, and Parkinson's disease were participated and their utterances were recorded. The utterances of 37 words from the Assessment of Phonology and Articulation for Children test were recorded in a quiet control booth in both groups. Patients were asked to repeat the recordings for evaluating the test–retest reliability. Patients' utterances were evaluated by two experienced SLPs, and the consonant production accuracy was calculated as a measure of dysarthria severity. The trained HMMs were also employed to evaluate the patients' utterances by calculating the averaged log likelihood (aLL) as the fitness of the spoken word to the word-specific HMM. RESULTS: The consonant production accuracy reported by the SLPs strongly correlated (r = 0.808) with the aLL, and the aLL showed excellent test–retest reliability (intraclass correlation coefficient, 0.964). CONCLUSION: This leads to the conclusion that dysarthria assessment using a one-word speech recognition system based on word-specific HMMs is feasible in neurological disorders.