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arXiv · 2208.06183

Non-Autoregressive Sign Language Production via Knowledge Distillation

Eui Jun Hwang·Jung Ho Kim·Suk Min Cho·Jong C. Park
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Abstract

Sign Language Production (SLP) aims to translate expressions in spoken language into corresponding ones in sign language, such as skeleton-based sign poses or videos. Existing SLP models are either AutoRegressive (AR) or Non-Autoregressive (NAR). However, AR-SLP models suffer from regression to the mean and error propagation during decoding. NSLP-G, a NAR-based model, resolves these issues to some extent but engenders other problems. For example, it does not consider target sign lengths and suffers from false decoding initiation. We propose a novel NAR-SLP model via Knowledge Distillation (KD) to address these problems. First, we devise a length regulator to predict the end of the generated sign pose sequence. We then adopt KD, which distills spatial-linguistic features from a pre-trained pose encoder to alleviate false decoding initiation. Extensive experiments show that the proposed approach significantly outperforms existing SLP models in both Frechet Gesture Distance and Back-Translation evaluation.

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Eui Jun Hwang, Jung Ho Kim, Suk Min Cho, Jong C. Park. 2022-08-12. Non-Autoregressive Sign Language Production via Knowledge Distillation. https://arxiv.org/abs/2208.06183

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