Recently, end-to-end models have become a popular approach as an alternative
to traditional hybrid models in automatic speech recognition (ASR). The
multi-speaker speech separation and recognition task is a central task in
cocktail party problem. In this paper, we present a state-of-the-art monaural
multi-speaker end-to-end automatic speech recognition model. In contrast to
previous studies on the monaural multi-speaker speech recognition, this
end-to-end framework is trained to recognize multiple label sequences
completely from scratch. The system only requires the speech mixture and
corresponding label sequences, without needing any indeterminate supervisions
obtained from non-mixture speech or corresponding labels/alignments. Moreover,
we exploited using the individual attention module for each separated speaker
and the scheduled sampling to further improve the performance. Finally, we
evaluate the proposed model on the 2-speaker mixed speech generated from the
WSJ corpus and the wsj0-2mix dataset, which is a speech separation and
recognition benchmark. The experiments demonstrate that the proposed methods
can improve the performance of the end-to-end model in separating the
overlapping speech and recognizing the separated streams. From the results, the
proposed model leads to ~10.0% relative performance gains in terms of CER and
WER respectively.

本文提出了一种最先进的单声道多说话者端到端自动语音识别模型，通过利用单个注意力模块为每个分离的说话者和调度抽样进一步提高性能，实验表明该方法可以在分离重叠的语音和识别分离的流方面提高端到端模型的性能。