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%).

深度学习在语音分离方面的应用及其对多说话人语音识别的整合进行了深入研究，并提出了一种通过复杂谱映射和自监督学习表示进行的训练策略，从而在噪声和混响环境下显著提升多说话人语音识别性能。

探索自监督学习表征中的语音分离与识别集成

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

Distant speech recognition is being revolutionized by deep learning, that has
contributed to significantly outperform previous HMM-GMM systems. A key aspect
behind the rapid rise and success of DNNs is their ability to better manage
large time contexts. With this regard, asymmetric context windows that embed
more past than future frames have been recently used with feed-forward neural
networks. This context configuration turns out to be useful not only to address
low-latency speech recognition, but also to boost the recognition performance
under reverberant conditions. This paper investigates on the mechanisms
occurring inside DNNs, which lead to an effective application of asymmetric
contexts.In particular, we propose a novel method for automatic context window
composition based on a gradient analysis. The experiments, performed with
different acoustic environments, features, DNN architectures, microphone
settings, and recognition tasks show that our simple and efficient strategy
leads to a less redundant frame configuration, which makes DNN training more
effective in reverberant scenarios.

本研究探讨了基于深度学习的远程语音识别中，非对称上下文窗口的机制，并提出了一种新的基于梯度分析的自动上下文窗口组合方法，结果表明此方法能够减少冗余帧配置，在混响环境下提供有效的语音识别性能。