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arXiv · 2506.03722

MFLA: Monotonic Finite Look-ahead Attention for Streaming Speech Recognition

Yinfeng Xia·Huiyan Li·Chenyang Le·Manhong Wang·Yutao Sun·Xingyang Ma·Yanmin Qian
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Abstract

Applying large pre-trained speech models like Whisper has shown promise in reducing training costs for various speech tasks. However, integrating these models into streaming systems remains a challenge. This paper presents a novel prefix-to-prefix training framework for streaming recognition by fine-tuning the Whisper. We introduce the Continuous Integrate-and-Fire mechanism to establish a quasi-monotonic alignment between continuous speech sequences and discrete text tokens. Additionally, we design Monotonic Finite Look-ahead Attention, allowing each token to attend to infinite left-context and finite right-context from the speech sequences. We also employ the wait-k decoding strategy to simplify the decoding process while ensuring consistency between training and testing. Our theoretical analysis and experiments demonstrate that this approach achieves a controllable trade-off between latency and quality, making it suitable for various streaming applications.

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Yinfeng Xia, Huiyan Li, Chenyang Le, Manhong Wang, Yutao Sun, Xingyang Ma, Yanmin Qian. 2025-06-04. MFLA: Monotonic Finite Look-ahead Attention for Streaming Speech Recognition. https://arxiv.org/abs/2506.03722

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