Cross-modal Audio-visual Co-learning for Text-independent Speaker Verification

• URL: http://arxiv.org/abs/2302.11254v1
• Date: Wed, 22 Feb 2023 10:06:37 GMT
• Title: Cross-modal Audio-visual Co-learning for Text-independent Speaker Verification
• Authors: Meng Liu, Kong Aik Lee, Longbiao Wang, Hanyi Zhang, Chang Zeng, Jianwu Dang
• Abstract summary: This paper proposes a cross-modal speech co-learning paradigm. Two cross-modal boosters are introduced based on an audio-visual pseudo-siamese structure to learn the modality-transformed correlation. Experimental results on the LRSLip3, GridLip, LomGridLip, and VoxLip datasets demonstrate that our proposed method achieves 60% and 20% average relative performance improvement.
• Score: 55.624946113550195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual speech (i.e., lip motion) is highly related to auditory speech due to the co-occurrence and synchronization in speech production. This paper investigates this correlation and proposes a cross-modal speech co-learning paradigm. The primary motivation of our cross-modal co-learning method is modeling one modality aided by exploiting knowledge from another modality. Specifically, two cross-modal boosters are introduced based on an audio-visual pseudo-siamese structure to learn the modality-transformed correlation. Inside each booster, a max-feature-map embedded Transformer variant is proposed for modality alignment and enhanced feature generation. The network is co-learned both from scratch and with pretrained models. Experimental results on the LRSLip3, GridLip, LomGridLip, and VoxLip datasets demonstrate that our proposed method achieves 60% and 20% average relative performance improvement over independently trained audio-only/visual-only and baseline fusion systems, respectively.

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