MFLA: Monotonic Finite Look-ahead Attention for Streaming Speech Recognition
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866915324546252800 |
|---|---|
| author | Xia, Yinfeng Li, Huiyan Le, Chenyang Wang, Manhong Sun, Yutao Ma, Xingyang Qian, Yanmin |
| author_facet | Xia, Yinfeng Li, Huiyan Le, Chenyang Wang, Manhong Sun, Yutao Ma, Xingyang Qian, Yanmin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_03722 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MFLA: Monotonic Finite Look-ahead Attention for Streaming Speech Recognition Xia, Yinfeng Li, Huiyan Le, Chenyang Wang, Manhong Sun, Yutao Ma, Xingyang Qian, Yanmin Computation and Language Sound Audio and Speech Processing 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. |
| title | MFLA: Monotonic Finite Look-ahead Attention for Streaming Speech Recognition |
| topic | Computation and Language Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2506.03722 |