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arXiv · 2305.13716

BA-SOT: Boundary-Aware Serialized Output Training for Multi-Talker ASR

Yuhao Liang·Fan Yu·Yangze Li·Pengcheng Guo·Shiliang Zhang·Qian Chen·Lei Xie
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Abstract

The recently proposed serialized output training (SOT) simplifies multi-talker automatic speech recognition (ASR) by generating speaker transcriptions separated by a special token. However, frequent speaker changes can make speaker change prediction difficult. To address this, we propose boundary-aware serialized output training (BA-SOT), which explicitly incorporates boundary knowledge into the decoder via a speaker change detection task and boundary constraint loss. We also introduce a two-stage connectionist temporal classification (CTC) strategy that incorporates token-level SOT CTC to restore temporal context information. Besides typical character error rate (CER), we introduce utterance-dependent character error rate (UD-CER) to further measure the precision of speaker change prediction. Compared to original SOT, BA-SOT reduces CER/UD-CER by 5.1%/14.0%, and leveraging a pre-trained ASR model for BA-SOT model initialization further reduces CER/UD-CER by 8.4%/19.9%.

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Yuhao Liang, Fan Yu, Yangze Li, Pengcheng Guo, Shiliang Zhang, Qian Chen, Lei Xie. 2023-05-23. BA-SOT: Boundary-Aware Serialized Output Training for Multi-Talker ASR. https://arxiv.org/abs/2305.13716

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