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arXiv · 2302.11254

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

Meng Liu·Kong Aik Lee·Longbiao Wang·Hanyi Zhang·Chang Zeng·Jianwu Dang
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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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BibTeXRIS

Meng Liu, Kong Aik Lee, Longbiao Wang, Hanyi Zhang, Chang Zeng, Jianwu Dang. 2023-02-22. Cross-modal Audio-visual Co-learning for Text-independent Speaker Verification. https://arxiv.org/abs/2302.11254

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