Cascade-Free Mandarin Visual Speech Recognition via Semantic-Guided Cross-Representation Alignment

2026-03-23

AI Summary

This work addresses the limitations of sequence-to-sequence models in Mandarin visual speech recognition, which stem from tonal characteristics, as well as error propagation and inference latency inherent in cascaded architectures. To overcome these challenges, the authors propose a non-cascaded multi-task learning framework that jointly models intermediate representations such as phonemes and visemes. The approach incorporates a semantics-guided local contrastive loss to achieve temporal alignment and on-demand activation of cross-modal features. Experimental results on public datasets demonstrate that the proposed model significantly outperforms existing methods, achieving higher recognition accuracy while effectively mitigating error propagation and improving inference efficiency.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Chinese mandarin visual speech recognition (VSR) is a task that has advanced in recent years, yet still lags behind the performance on non-tonal languages such as English. One primary challenge arises from the tonal nature of Mandarin, which limits the effectiveness of conventional sequence-to-sequence modeling approaches. To alleviate this issue, existing Chinese VSR systems commonly incorporate intermediate representations, most notably pinyin, within cascade architectures to enhance recognition accuracy. While beneficial, in these cascaded designs, the subsequent stage during inference depends on the output of the preceding stage, leading to error accumulation and increased inference latency. To address these limitations, we propose a cascade-free architecture based on multitask learning that jointly integrates multiple intermediate representations, including phoneme and viseme, to better exploit contextual information. The proposed semantic-guided local contrastive loss temporally aligns the features, enabling on-demand activation during inference, thereby providing a trade-off between inference efficiency and performance while mitigating error accumulation caused by projection and re-embedding. Experiments conducted on publicly available datasets demonstrate that our method achieves superior recognition performance.

Problem

• Mandarin visual speech recognition
• tonal language
• cascade architecture
• error accumulation
• inference latency

Innovation

• cascade-free
• multitask learning
• semantic-guided alignment
• visual speech recognition
• Mandarin tonal modeling