An end-to-end integration of speech separation and recognition with self-supervised learning representation

Multi-speaker automatic speech recognition (ASR) has gained growing attention in a wide range of applications, including conversation analysis and human-computer interaction. Speech separation and enhancement (SSE) and single-speaker ASR have witnessed remarkable performance improvements with the rapid advances in deep learning. Complex spectral mapping predicts the short-time Fourier transform (STFT) coefficients of each speaker and has achieved promising results in several SSE benchmarks. Meanwhile, self-supervised learning representation (SSLR) has demonstrated its significant advantage in single-speaker ASR. In this work, we push forward the performance of multi-speaker ASR under noisy reverberant conditions by integrating powerful SSE, SSL, and ASR models in an end-to-end manner. We systematically investigate both monaural and multi-channel SSE methods and various feature representations. Our experiments demonstrate the advantages of recently proposed complex spectral mapping and SSLRs in multi-speaker ASR. The experimental results also confirm that end-to-end finetuning with an ASR criterion is important to achieve state-of-the-art word error rates (WERs) even with powerful pre-trained models. Moreover, we show the performance trade-off between SSE and ASE and mitigate it with a multi-task learning framework with both SSE and ASR criteria.