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

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