Audio-Visual Speaker Verification via Joint Cross-Attention
R. Gnana Praveen Jahangir Alam
Speaker verification has been widely explored using speech signals, which has
shown significant improvement using deep models. Recently, there has been a
surge in exploring faces and voices as they can offer more complementary and
comprehensive information than relying only on a single modality of speech
signals. Though current methods in the literature on the fusion of faces and
voices have shown improvement over that of individual face or voice modalities,
the potential of audio-visual fusion is not fully explored for speaker
verification. Most of the existing methods based on audio-visual fusion either
rely on score-level fusion or simple feature concatenation. In this work, we
have explored cross-modal joint attention to fully leverage the inter-modal
complementary information and the intra-modal information for speaker
verification. Specifically, we estimate the cross-attention weights based on
the correlation between the joint feature presentation and that of the
individual feature representations in order to effectively capture both
intra-modal as well inter-modal relationships among the faces and voices. We
have shown that efficiently leveraging the intra- and inter-modal relationships
significantly improves the performance of audio-visual fusion for speaker
verification. The performance of the proposed approach has been evaluated on
the Voxceleb1 dataset. Results show that the proposed approach can
significantly outperform the state-of-the-art methods of audio-visual fusion
for speaker verification.