Cross-Speaker Encoding Network for Multi-Talker Speech Recognition

Autor:	Kang, Jiawen, Meng, Lingwei, Cui, Mingyu, Guo, Haohan, Wu, Xixin, Liu, Xunying, Meng, Helen
Rok vydání:	2024
Předmět:	Computer Science - Sound Computer Science - Artificial Intelligence Computer Science - Computation and Language Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
DOI:	10.1109/ICASSP48485.2024.10446249
Popis:	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. Code is available at https://github.com/kjw11/CSEnet-ASR. Comment: Accepted by ICASSP2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2401.04152 Zobrazit plný text záznamu View this record from Arxiv