Multistream neural architectures for cued-speech recognition using a pre-trained visual feature extractor and constrained CTC decoding

• URL: http://arxiv.org/abs/2204.04965v1
• Date: Mon, 11 Apr 2022 09:30:08 GMT
• Title: Multistream neural architectures for cued-speech recognition using a pre-trained visual feature extractor and constrained CTC decoding
• Authors: Sanjana Sankar (GIPSA-CRISSP), Denis Beautemps (GIPSA-CRISSP), Thomas Hueber (GIPSA-CRISSP)
• Abstract summary: Cued Speech (CS) is a visual communication tool that helps people with hearing impairment to understand spoken language. The proposed approach is based on a pre-trained hand and lips tracker used for visual feature extraction and a phonetic decoder based on a multistream recurrent neural network. With a decoding accuracy at the phonetic level of 70.88%, the proposed system outperforms our previous CNN-HMM decoder and competes with more complex baselines.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes a simple and effective approach for automatic recognition of Cued Speech (CS), a visual communication tool that helps people with hearing impairment to understand spoken language with the help of hand gestures that can uniquely identify the uttered phonemes in complement to lipreading. The proposed approach is based on a pre-trained hand and lips tracker used for visual feature extraction and a phonetic decoder based on a multistream recurrent neural network trained with connectionist temporal classification loss and combined with a pronunciation lexicon. The proposed system is evaluated on an updated version of the French CS dataset CSF18 for which the phonetic transcription has been manually checked and corrected. With a decoding accuracy at the phonetic level of 70.88%, the proposed system outperforms our previous CNN-HMM decoder and competes with more complex baselines.

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