We develop an end-to-end system for multi-channel, multi-speaker automatic
speech recognition. We propose a frontend for joint source separation and
dereverberation based on the independent vector analysis (IVA) paradigm. It
uses the fast and stable iterative source steering algorithm together with a
neural source model. The parameters from the ASR module and the neural source
model are optimized jointly from the ASR loss itself. We demonstrate
competitive performance with previous systems using neural beamforming
frontends. First, we explore the trade-offs when using various number of
channels for training and testing. Second, we demonstrate that the proposed IVA
frontend performs well on noisy data, even when trained on clean mixtures only.
Furthermore, it extends without retraining to the separation of more speakers,
which is demonstrated on mixtures of three and four speakers.

本研究提出了一个端到端的多通道、多人自动语音识别系统，其中前端基于独立向量分析（IVA）范例的联合源分离和去混响机制，实现了与先前使用神经束形成前端的系统具有竞争性的性能，并能够扩展到更多说话人的分离。