Audio-Visual Speaker Verification via Joint Cross-Attention-Reference-Cited by-同舟云学术

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Audio-Visual Speaker Verification via Joint Cross-Attention

Published:2023 Issue: Volume: Page:18-31
ISSN:0302-9743
Container-title:Speech and Computer
language:
Short-container-title:

Author:

Rajasekhar Gnana Praveen,Alam Jahangir

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-48312-7_2

Reference60 articles.

1. Alam, J., Fathan, A., Kang, W.H.: Text-independent speaker verification employing CNN-LSTM-TDNN hybrid networks. In: Speech and Computer, pp. 1–13 (2021)

2. Alam, J., Kang, W.H., Fathan, A.: Hybrid neural network with cross- and self-module attention pooling for text-independent speaker verification. In: IEEE ICASSP, pp. 1–5 (2023)

3. Chen, H., Zhang, H., Wang, L., Lee, K.A., Liu, M., Dang, J.: Self-supervised audio-visual speaker representation with co-meta learning. In: IEEE ICASSP, pp. 1–5 (2023)

4. Chen, J., Chen, Z., Chi, Z., Fu, H.: Emotion recognition in the wild with feature fusion and multiple kernel learning. In: ICMI, pp. 508–513 (2014)

5. Chen, Z., Wang, S., Qian, Y.: Multi-modality matters: a performance leap on VoxCeleb. In: Proceedings of Interspeech, pp. 2252–2256 (2020)

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