Exploring the Integration of Speech Separation and Recognition with Self-Supervised Learning Representation

• URL: http://arxiv.org/abs/2307.12231v1
• Date: Sun, 23 Jul 2023 05:39:39 GMT
• Title: Exploring the Integration of Speech Separation and Recognition with Self-Supervised Learning Representation
• Authors: Yoshiki Masuyama, Xuankai Chang, Wangyou Zhang, Samuele Cornell, Zhong-Qiu Wang, Nobutaka Ono, Yanmin Qian, Shinji Watanabe
• Abstract summary: This work provides an insightful investigation of speech separation in reverberant and noisy-reverberant scenarios as an ASR front-end. 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. A 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.
• Score: 83.36685075570232
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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%).

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