3 papers
eess.AS2023
A Single Speech Enhancement Model Unifying Dereverberation, Denoising, Speaker Counting, Separation, and Extraction
Kohei Saijo, Wangyou Zhang, Zhong-Qiu Wang +3
We propose a multi-task universal speech enhancement (MUSE) model that can perform five speech enhancement (SE) tasks: dereverberation, denoising, speech separation (SS), target sp…
cs.SD2023
Mask-CTC-based Encoder Pre-training for Streaming End-to-End Speech Recognition
Huaibo Zhao, Yosuke Higuchi, Yusuke Kida +2
Achieving high accuracy with low latency has always been a challenge in streaming end-to-end automatic speech recognition (ASR) systems. By attending to more future contexts, a str…
eess.AS2023
Remixing-based Unsupervised Source Separation from Scratch
Kohei Saijo, Tetsuji Ogawa
We propose an unsupervised approach for training separation models from scratch using RemixIT and Self-Remixing, which are recently proposed self-supervised learning methods for re…