Automatic Intelligibility Assessment of Dysarthric Speech Using Phonologically-Structured Sparse Linear Model

Abstract

This paper presents a new method for automatically assessing the speech intelligibility of patients with dysarthria, which is a motor speech disorder impeding the physical production of speech. The proposed method consists of two main steps: feature representation and prediction. In the feature representation step, the speech utterance is converted into a phone sequence using an automatic speech recognition technique and is then aligned with a canonical phone sequence from a pronunciation dictionary using a weighted finite state transducer to capture the pronunciation mappings such as match, substitution, and deletion. The histograms of the pronunciation mappings on a pre-defined word set are used for features. Next, in the prediction step, a structured sparse linear model incorporated with phonological knowledge that simultaneously addresses phonologically structured sparse feature selection and intelligibility prediction is proposed. Evaluation of the proposed method on a database of 109 speakers consisting of 94 dysarthric and 15 control speakers yielded a root mean square error of 8.14 compared to subjectively rated scores in the range of 0 to 100. This is a promising performance in which the system can be successfully applied to help speech therapists in diagnosing the degree of speech disorder.