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arXiv · 2505.22069

Delayed-KD: Delayed Knowledge Distillation based CTC for Low-Latency Streaming ASR

Longhao Li·Yangze Li·Hongfei Xue·Jie Liu·Shuai Fang·Kai Wang·Lei Xie
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Abstract

CTC-based streaming ASR has gained significant attention in real-world applications but faces two main challenges: accuracy degradation in small chunks and token emission latency. To mitigate these challenges, we propose Delayed-KD, which applies delayed knowledge distillation on CTC posterior probabilities from a non-streaming to a streaming model. Specifically, with a tiny chunk size, we introduce a Temporal Alignment Buffer (TAB) that defines a relative delay range compared to the non-streaming teacher model to align CTC outputs and mitigate non-blank token mismatches. Additionally, TAB enables fine-grained control over token emission delay. Experiments on 178-hour AISHELL-1 and 10,000-hour WenetSpeech Mandarin datasets show consistent superiority of Delayed-KD. Impressively, Delayed-KD at 40 ms latency achieves a lower character error rate (CER) of 5.42% on AISHELL-1, comparable to the competitive U2++ model running at 320 ms latency.

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Longhao Li, Yangze Li, Hongfei Xue, Jie Liu, Shuai Fang, Kai Wang, Lei Xie. 2025-05-28. Delayed-KD: Delayed Knowledge Distillation based CTC for Low-Latency Streaming ASR. https://arxiv.org/abs/2505.22069

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