A discriminative training method incorporating pronunciation variations for dysarthric automatic speech recognition

Abstract

While dysarthric speech recognition can be a convenient interface for dysarthric speakers, it is hard to collect enough speech data to overcome the underestimation problem of acoustic models. In addition, there are lots of pronunciation variations in the collected database due to the paralysis of the articulator of dysarthric speakers. Thus, a discriminative training method is proposed for improving the performance of such resource-limited dysarthric speech recognition. The proposed method is applied to subspace Gaussian mixture modeling by incorporating pronunciation variations into a conventional minimum phone error discriminative training method.