Latent Phrase Matching for Dysarthric Speech
Colin Lea Dianna Yee Jaya Narain Zifang Huang Lauren Tooley Jeffrey P. Bigham Leah Findlater
Many consumer speech recognition systems are not tuned for people with speech
disabilities, resulting in poor recognition and user experience, especially for
severe speech differences. Recent studies have emphasized interest in
personalized speech models from people with atypical speech patterns. We
propose a query-by-example-based personalized phrase recognition system that is
trained using small amounts of speech, is language agnostic, does not assume a
traditional pronunciation lexicon, and generalizes well across speech
difference severities. On an internal dataset collected from 32 people with
dysarthria, this approach works regardless of severity and shows a 60%
improvement in recall relative to a commercial speech recognition system. On
the public EasyCall dataset of dysarthric speech, our approach improves
accuracy by 30.5%. Performance degrades as the number of phrases increases, but
consistently outperforms ASR systems when trained with 50 unique phrases.