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

arXiv: https://arxiv.org/abs/2204.04965 · HTML: https://mlsanity.com/paper/2204.04965/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 11 Apr 2022.

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Re-synchronization using the Hand Preceding Model for Multi-modal Fusion in Automatic Continuous Cued Speech Recognition](https://mlsanity.com/paper/2001.00854/) (2020)
- [Cross-Modal Knowledge Distillation Method for Automatic Cued Speech Recognition](https://mlsanity.com/paper/2106.13686/) (2021)

## Built on by (most cited first, 7 of 7)

- [Cross-Modal Mutual Learning for Cued Speech Recognition](https://mlsanity.com/paper/2212.01083/) (2022)
- [A Novel Interpretable and Generalizable Re-synchronization Model for Cued Speech based on a Multi-Cuer Corpus](https://mlsanity.com/paper/2306.02596/) (2023)
- [A Survey on Deep Multi-modal Learning for Body Language Recognition and Generation](https://mlsanity.com/paper/2308.08849/) (2023)
- [Computation and Parameter Efficient Multi-Modal Fusion Transformer for Cued Speech Recognition](https://mlsanity.com/paper/2401.17604/) (2024)
- [Investigating the dynamics of hand and lips in French Cued Speech using attention mechanisms and CTC-based decoding](https://mlsanity.com/paper/2306.08290/) (2023)
- [Cued Speech Generation Leveraging a Pre-trained Audiovisual Text-to-Speech Model](https://mlsanity.com/paper/2501.04799/) (2025)
- [Cued-Agent: A Collaborative Multi-Agent System for Automatic Cued Speech Recognition](https://mlsanity.com/paper/2508.00391/) (2025)

## Abstract (arXiv)

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.