End-to-end multi-talker speech recognition has garnered great interest as an
effective approach to directly transcribe overlapped speech from multiple
speakers. Current methods typically adopt either 1) single-input
multiple-output (SIMO) models with a branched encoder, or 2) single-input
single-output (SISO) models based on attention-based encoder-decoder
architecture with serialized output training (SOT). In this work, we propose a
Cross-Speaker Encoding (CSE) network to address the limitations of SIMO models
by aggregating cross-speaker representations. Furthermore, the CSE model is
integrated with SOT to leverage both the advantages of SIMO and SISO while
mitigating their drawbacks. To the best of our knowledge, this work represents
an early effort to integrate SIMO and SISO for multi-talker speech recognition.
Experiments on the two-speaker LibrispeechMix dataset show that the CES model
reduces word error rate (WER) by 8% over the SIMO baseline. The CSE-SOT model
reduces WER by 10% overall and by 16% on high-overlap speech compared to the
SOT model.

这项工作提出了一个跨说话人编码（Cross-Speaker Encoding，CSE）网络，将单输入多输出（SIMO）模型的局限性通过集成串行输出训练（SOT）的基于注意力的编码器 - 解码器架构的单输入单输出（SISO）模型来解决，首次尝试将 SIMO 和 SISO 整合进多说话人语音识别中，实验结果表明 CSE-SOT 模型在 LibrispeechMix 数据集上比 SOT 模型的词错误率（WER）平均降低 10％，在高重叠语音上降低 16％。