Improving the Gap in Visual Speech Recognition Between Normal and Silent Speech Based on Metric Learning

TL;DR

This paper introduces a metric learning approach for visual speech recognition that reduces the performance gap between normal and silent speech by aligning viseme representations in a shared latent space, improving silent speech recognition accuracy.

Contribution

The paper proposes a novel metric learning method based on visemes to enhance silent speech recognition, addressing data scarcity and model performance issues.

Findings

Improved silent speech recognition accuracy.

Effective viseme-based latent space mapping.

Robust performance with limited training data.

Abstract

This paper presents a novel metric learning approach to address the performance gap between normal and silent speech in visual speech recognition (VSR). The difference in lip movements between the two poses a challenge for existing VSR models, which exhibit degraded accuracy when applied to silent speech. To solve this issue and tackle the scarcity of training data for silent speech, we propose to leverage the shared literal content between normal and silent speech and present a metric learning approach based on visemes. Specifically, we aim to map the input of two speech types close to each other in a latent space if they have similar viseme representations. By minimizing the Kullback-Leibler divergence of the predicted viseme probability distributions between and within the two speech types, our model effectively learns and predicts viseme identities. Our evaluation demonstrates that our method improves the accuracy of silent VSR, even when limited training data is available.