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The role of a sign interpreting agent is to bridge the communication gap between the hearing-only and Deaf or Hard of Hearing communities by translating both from sign language to text and from text to sign language. Until now, much of the AI work in automated sign language processing has focused primarily on sign language to text translation, which puts the advantage mainly on the side of hearing individuals. In this work, we describe advances in sign language processing based on transformer networks. Specifically, we introduce SignNet II, a sign language processing architecture, a promising step towards facilitating two-way sign language communication. It is comprised of sign-to-text and text-to-sign networks jointly trained using a dual learning mechanism. Furthermore, by exploiting the notion of sign similarity, a metric embedding learning process is introduced to enhance the text-to-sign translation performance. Using a bank of multi-feature transformers, we analyzed several input feature representations and discovered that keypoint-based pose features consistently performed well, irrespective of the quality of the input videos. We demonstrated that the two jointly trained networks outperformed their singly-trained counterparts, showing noteworthy enhancements in BLEU-1 - BLEU-4 scores when tested on the largest available German Sign Language (GSL) benchmark dataset.
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Effects of Feature Scaling and Fusion on Sign Language Translation
Sign language translation without transcription has only recently started to gain attention. In our work, we focus on improving the state-of-the-art translation by introducing a multi-feature fusion architecture with enhanced input features. As sign language is challenging to segment, we obtain the input features by extracting overlapping scaled segments across the video and obtaining their 3D CNN representations. We exploit the attention mechanism in the fusion architecture by initially learning dependencies between different frames of the same video and later fusing them to learn the relations between different features from the same video. In addition to 3D CNN features, we also analyze pose-based features. Our robust methodology outperforms the state-of-the-art sign language translation model by achieving higher BLEU 3 – BLEU 4 scores and also outperforms the state-of-the-art sequence attention models by achieving a 43.54% increase in BLEU 4 score. We conclude that the combined effects of feature scaling and feature fusion make our model more robust in predicting longer n-grams which are crucial in continuous sign language translation.
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- Award ID(s):
- 1846076
- PAR ID:
- 10321200
- Date Published:
- Journal Name:
- Proceedings of Interspeech 2021
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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A true interpreting agent not only understands sign language and translates to text, but also understands text and translates to signs. Much of the AI work in sign language translation to date has focused mainly on translating from signs to text. Towards the latter goal, we propose a text-to-sign translation model, SignNet, which exploits the notion of similarity (and dissimilarity) of visual signs in translating. This module presented is only one part of a dual-learning two task process involving text-to-sign (T2S) as well as sign-to-text (S2T). We currently implement SignNet as a single channel architecture so that the output of the T2S task can be fed into S2T in a continuous dual learning framework. By single channel, we refer to a single modality, the body pose joints. In this work, we present SignNet, a T2S task using a novel metric embedding learning process, to preserve the distances between sign embeddings relative to their dissimilarity. We also describe how to choose positive and negative examples of signs for similarity testing. From our analysis, we observe that metric embedding learning-based model perform significantly better than the other models with traditional losses, when evaluated using BLEU scores. In the task of gloss to pose, SignNet performed as well as its state-of-the-art (SoTA) counterparts and outperformed them in the task of text to pose, by showing noteworthy enhancements in BLEU 1 - BLEU 4 scores (BLEU 1: 31 → 39; ≈26% improvement and BLEU 4: 10.43 →11.84; ≈14% improvement) when tested on the popular RWTH PHOENIX-Weather-2014T benchmark datasetmore » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.21437/Interspeech.2021-1863
