Intelligibility Assessment and Speech Recognizer Word Accuracy Rate Prediction for Dysarthric Speakers in a Factor Analysis Subspace

Abstract

Automated intelligibility assessments can support speech and language therapists in determining the type of dysarthria presented by their clients. Such assessments can also help predict how well a person with dysarthria might cope with a voice interface to assistive technology. Our approach to intelligibility assessment is based on iVectors , a set of measures that capture many aspects of a person’s speech, including intelligibility. The major advantage of iVectors is that they compress all acoustic information contained in an utterance into a reduced number of measures, and they are very suitable to be used with simple predictors. We show that intelligibility assessments work best if there is a pre-existing set of words annotated for intelligibility from the speaker to be evaluated, which can be used for training our system. We discuss the implications of our findings for practice.