Automatic Speech Recognition of Disordered Speech: Personalized models outperforming human listeners on short phrases

Objective. This study aimed to (1) evaluate the performance of personalized Automatic Speech Recognition (ASR) models on disordered speech samples representing a wide range of etiologies and speech severities, and (2) compare the accuracy of these models to that of speaker-independent ASR models developed on and for typical speech as well as expert human listeners. Methods. 432 individuals with self-reported disordered speech recorded at least 300 short phrases using a web-based application. Word error rates (WER) were computed using three different ASR models and expert human transcribers. Metadata were collected to evaluate the potential impact of participant, atypical speech, and technical factors on recognition accuracy. Results. The accuracy of personalized models for recognizing disordered speech was high (WER: 4.6%), and significantly better than speaker-independent models (WER: 31%). Personalized models also outperformed human transcribers (WER gain: 9%) with relative gains in accuracy as high as 80%. The most significant gain in recognition performance was for the most severely affected speakers. Low SNR and fewer training utterances adversely affected recognition even for speakers with mild speech impairments. Conclusions. Personalized ASR models have significant potential for improving communication for persons with impaired speech.

• Responsible AI

• Speech Processing