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Electrical Engineering and Systems Science > Audio and Speech Processing

arXiv:2309.10299 (eess)
[Submitted on 19 Sep 2023]

Title:Using fine-tuning and min lookahead beam search to improve Whisper

Authors:Andrea Do, Oscar Brown, Zhengjie Wang, Nikhil Mathew, Zixin Liu, Jawwad Ahmed, Cheng Yu
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Abstract:The performance of Whisper in low-resource languages is still far from perfect. In addition to a lack of training data on low-resource languages, we identify some limitations in the beam search algorithm used in Whisper. To address these issues, we fine-tune Whisper on additional data and propose an improved decoding algorithm. On the Vietnamese language, fine-tuning Whisper-Tiny with LoRA leads to an improvement of 38.49 in WER over the zero-shot Whisper-Tiny setting which is a further reduction of 1.45 compared to full-parameter fine-tuning. Additionally, by using Filter-Ends and Min Lookahead decoding algorithms, the WER reduces by 2.26 on average over a range of languages compared to standard beam search. These results generalise to larger Whisper model sizes. We also prove a theorem that Min Lookahead outperforms the standard beam search algorithm used in Whisper.
Comments: 8 pages, submitted to IEEE ICASSP 2024
Subjects: Audio and Speech Processing (eess.AS); Computation and Language (cs.CL); Machine Learning (cs.LG); Sound (cs.SD)
Cite as: arXiv:2309.10299 [eess.AS]
  (or arXiv:2309.10299v1 [eess.AS] for this version)
  https://doi.org/10.48550/arXiv.2309.10299

Submission history

From: Andrea Do [view email]
[v1] Tue, 19 Sep 2023 04:04:14 UTC (54 KB)
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