Exploring the Integration of Speech Separation and Recognition with
  Self-Supervised Learning Representation
Yoshiki Masuyama Xuankai Chang Wangyou Zhang Samuele Cornell Zhong-Qiu Wang Nobutaka Ono Yanmin Qian Shinji Watanabe
Neural speech separation has made remarkable progress and its integration
with automatic speech recognition (ASR) is an important direction towards
realizing multi-speaker ASR. This work provides an insightful investigation of
speech separation in reverberant and noisy-reverberant scenarios as an ASR
front-end. In detail, we explore multi-channel separation methods, mask-based
beamforming and complex spectral mapping, as well as the best features to use
in the ASR back-end model. We employ the recent self-supervised learning
representation (SSLR) as a feature and improve the recognition performance from
the case with filterbank features. To further improve multi-speaker recognition
performance, we present a carefully designed training strategy for integrating
speech separation and recognition with SSLR. The proposed integration using
TF-GridNet-based complex spectral mapping and WavLM-based SSLR achieves a 2.5%
word error rate in reverberant WHAMR! test set, significantly outperforming an
existing mask-based MVDR beamforming and filterbank integration (28.9%).