Continuous Sign Language Recognition Using Intra-inter Gloss Attention

TL;DR

This paper introduces intra-inter gloss attention modules in transformer-based models for continuous sign language recognition, effectively capturing local and semantic dependencies to improve accuracy without extra supervision.

Contribution

The study proposes a novel intra-inter gloss attention mechanism that enhances sequence modeling in CSLR by focusing on local chunks and gloss-level relationships, reducing complexity and noise.

Findings

Achieved a 20.4% WER on PHOENIX-2014 dataset.

Outperformed state-of-the-art methods without additional supervision.

Effectively captured local and semantic dependencies in sign language videos.

Abstract

Many continuous sign language recognition (CSLR) studies adopt transformer-based architectures for sequence modeling due to their powerful capacity for capturing global contexts. Nevertheless, vanilla self-attention, which serves as the core module of the transformer, calculates a weighted average over all time steps; therefore, the local temporal semantics of sign videos may not be fully exploited. In this study, we introduce a novel module in sign language recognition studies, called intra-inter gloss attention module, to leverage the relationships among frames within glosses and the semantic and grammatical dependencies between glosses in the video. In the intra-gloss attention module, the video is divided into equally sized chunks and a self-attention mechanism is applied within each chunk. This localized self-attention significantly reduces complexity and eliminates noise introduced by considering non-relative frames. In the inter-gloss attention module, we first aggregate the chunk-level features within each gloss chunk by average pooling along the temporal dimension. Subsequently, multi-head self-attention is applied to all chunk-level features. Given the non-significance of the signer-environment interaction, we utilize segmentation to remove the background of the videos. This enables the proposed model to direct its focus toward the signer. Experimental results on the PHOENIX-2014 benchmark dataset demonstrate that our method can effectively extract sign language features in an end-to-end manner without any prior knowledge, improve the accuracy of CSLR, and achieve the word error rate (WER) of 20.4 on the test set which is a competitive result compare to the state-of-the-art which uses additional supervisions.