2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDSPE-36.2
Paper Title VSET: A Multimodal Transformer for Visual Speech Enhancement
Authors Karthik Ramesh, Chao Xing, Wupeng Wang, Huawei, Canada; Dong Wang, Tsinghua University, China; Xiao Chen, Huawei, Hong Kong SAR China
SessionSPE-36: Speech Enhancement 6: Multi-modal Processing
LocationGather.Town
Session Time:Thursday, 10 June, 14:00 - 14:45
Presentation Time:Thursday, 10 June, 14:00 - 14:45
Presentation Poster
Topic Speech Processing: [SPE-ENHA] Speech Enhancement and Separation
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract The transformer architecture has shown great capability in learning long-term dependency and works well in multiple domains. However, transformer has been less considered in audio-visual speech enhancement (AVSE) research, partly due to the convention that treats speech enhancement as a short-time signal processing task. In this paper, we challenge this common belief and show that an audio-visual transformer can significantly improve AVSE performance, by learning the long-term dependency of both intra-modality and inter-modality. We test this new transformer-based AVSE model on the GRID and AVSpeech datasets, and show that it beats several state-of-the-art models by a large margin.