Separate in the Speech Chain: Cross-Modal Conditional Audio-Visual Target Speech Extraction
This work addresses a key challenge in multi-modal learning for speech extraction, offering improvements for applications like hearing aids or speech recognition in noisy environments, though it appears incremental as it builds on existing paradigms.
The paper tackled the problem of modality imbalance in audio-visual target speech extraction, where audio dominates over visual cues, by proposing AVSepChain, a two-stage method based on the speech chain concept, which achieved superior performance on multiple benchmark datasets.
The integration of visual cues has revitalized the performance of the target speech extraction task, elevating it to the forefront of the field. Nevertheless, this multi-modal learning paradigm often encounters the challenge of modality imbalance. In audio-visual target speech extraction tasks, the audio modality tends to dominate, potentially overshadowing the importance of visual guidance. To tackle this issue, we propose AVSepChain, drawing inspiration from the speech chain concept. Our approach partitions the audio-visual target speech extraction task into two stages: speech perception and speech production. In the speech perception stage, audio serves as the dominant modality, while visual information acts as the conditional modality. Conversely, in the speech production stage, the roles are reversed. This transformation of modality status aims to alleviate the problem of modality imbalance. Additionally, we introduce a contrastive semantic matching loss to ensure that the semantic information conveyed by the generated speech aligns with the semantic information conveyed by lip movements during the speech production stage. Through extensive experiments conducted on multiple benchmark datasets for audio-visual target speech extraction, we showcase the superior performance achieved by our proposed method.