Multi-Variant Consistency based Self-supervised Learning for Robust Automatic Speech Recognition

Understand

Automatic speech recognition (ASR) has shown rapid advances in recent years but still degrades significantly in far-field and noisy environments.

• The recent development of self-supervised learning (SSL) technology can improve the ASR performance by pre-training the model with additional unlabeled speech and the SSL pre-trained model has achieved the state-of-the-art result on several speech benchmarks.
• Nevertheless, most of the previous SSL methods ignore the influence of the background noise or reverberation, which is crucial to deploying ASR systems in real-world speech applications.
• This study addresses the robust ASR by introducing a multi-variant consistency (MVC) based SSL method that adapts to different environments.