Silent versus modal multi-speaker speech recognition from ultrasound and video

TL;DR

This study compares silent and modal multi-speaker speech recognition using ultrasound and video imaging, highlighting challenges due to mode mismatch and proposing adaptation techniques to improve silent speech recognition accuracy.

Contribution

The paper introduces methods to address domain mismatch in silent speech recognition and analyzes articulatory differences between silent and modal speech modes.

Findings

Silent speech recognition underperforms compared to modal speech.

Domain adaptation techniques improve silent speech recognition.

Silent speech has longer duration and smaller articulatory space.

Abstract

We investigate multi-speaker speech recognition from ultrasound images of the tongue and video images of the lips. We train our systems on imaging data from modal speech, and evaluate on matched test sets of two speaking modes: silent and modal speech. We observe that silent speech recognition from imaging data underperforms compared to modal speech recognition, likely due to a speaking-mode mismatch between training and testing. We improve silent speech recognition performance using techniques that address the domain mismatch, such as fMLLR and unsupervised model adaptation. We also analyse the properties of silent and modal speech in terms of utterance duration and the size of the articulatory space. To estimate the articulatory space, we compute the convex hull of tongue splines, extracted from ultrasound tongue images. Overall, we observe that the duration of silent speech is longer than that of modal speech, and that silent speech covers a smaller articulatory space than modal speech. Although these two properties are statistically significant across speaking modes, they do not directly correlate with word error rates from speech recognition.