Attention Wave-U-Net for Speech Enhancement
Ritwik Giri, Umut Isik, Arvindh Krishnaswamy · 2019
We propose a novel application of an attention mechanism in neural speech enhancement, by presenting a U-Net architecture with attention mechanism, which processes the raw waveform directly, and is trained end-to-end. We find that the inclusion of the attention mechanism significantly improves the performance of the model in terms of the objective speech quality metrics, and outperforms all other published speech enhancement approaches on the Voice Bank Corpus (VCTK) dataset. We observe that the final layer attention mask has an interpretation as a soft Voice Activity Detector (VAD). We also present some initial results to show the efficacy of the proposed system as a pre-processing step to speech recognition systems.