Cuing Without Sharing: A Federated Cued Speech Recognition Framework via Mutual Knowledge Distillation

TL;DR

This paper introduces a privacy-preserving federated learning framework for automatic cued speech recognition that leverages mutual knowledge distillation to effectively utilize decentralized data without sharing sensitive videos.

Contribution

It proposes a novel federated cued speech recognition framework with mutual knowledge distillation, enabling cross-modal semantic consistency and privacy protection in decentralized data settings.

Findings

Outperforms federated learning baselines and centralized methods.

Achieves 9.7% CER and 15.0% WER improvements.

First federated approach for ACSR with privacy considerations.

Abstract

Cued Speech (CS) is a visual coding tool to encode spoken languages at the phonetic level, which combines lip-reading and hand gestures to effectively assist communication among people with hearing impairments. The Automatic CS Recognition (ACSR) task aims to recognize CS videos into linguistic texts, which involves both lips and hands as two distinct modalities conveying complementary information. However, the traditional centralized training approach poses potential privacy risks due to the use of facial and gesture videos in CS data. To address this issue, we propose a new Federated Cued Speech Recognition (FedCSR) framework to train an ACSR model over the decentralized CS data without sharing private information. In particular, a mutual knowledge distillation method is proposed to maintain cross-modal semantic consistency of the Non-IID CS data, which ensures learning a unified feature space for both linguistic and visual information. On the server side, a globally shared linguistic model is trained to capture the long-term dependencies in the text sentences, which is aligned with the visual information from the local clients via visual-to-linguistic distillation. On the client side, the visual model of each client is trained with its own local data, assisted by linguistic-to-visual distillation treating the linguistic model as the teacher. To the best of our knowledge, this is the first approach to consider the federated ACSR task for privacy protection. Experimental results on the Chinese CS dataset with multiple cuers demonstrate that our approach outperforms both mainstream federated learning baselines and existing centralized state-of-the-art ACSR methods, achieving 9.7% performance improvement for character error rate (CER) and 15.0% for word error rate (WER).