Tokenizer Transplantation: Mitigating Autoregressive Collapse in Edge-Efficient Bengali ASR
Quick Answer
This study introduces a vocabulary transplantation pipeline to enhance Bengali ASR performance by replacing the English-centric tokenizer with BanglaBERT WordPiece vocabulary, achieving a 21.54% Word Error Rate and reducing autoregressive sequence length by 85.8%.
Quick Take
The approach mitigates decoding instability, making it a scalable solution for edge deployment in morphologically rich languages.
Key Points
- Novel pipeline replaces English tokenizer with BanglaBERT vocabulary for Bengali ASR.
- Achieved 21.54% Word Error Rate on the 882-hour Lipi-Ghor dataset.
- Reduced token fertility from 9.16 to 1.30, improving model stability.
- Decreased autoregressive sequence length by 85.8%, eliminating decoding instability.
- Provides a reproducible blueprint for cross-script adaptation of ASR models.
Paper Resources
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~2 min readAbstract:Lightweight speech recognition models are critical for edge deployment, yet highly optimized architectures like Moonshine often fail on morphologically rich, non-Latin languages such as Bengali. This study identifies the root cause of this failure as the model's English-centric byte-level tokenizer, which fragments Bengali words into high-fertility byte chains and triggers catastrophic autoregressive collapse during inference. To resolve this, a novel vocabulary transplantation pipeline is proposed to replace the decoder vocabulary with the native-script BanglaBERT WordPiece vocabulary and resize the corresponding token embedding matrix. Experimental results demonstrate a reduction in token fertility from 9.16 to 1.30. By decreasing autoregressive sequence length by 85.8%, decoding instability is entirely mitigated. When evaluated on the 882-hour Lipi-Ghor dataset, the modified architecture achieves a competitive 21.54% Word Error Rate (WER) and a Real-Time Factor (RTF) of 0.0053. Ultimately, this research provides a scalable, reproducible blueprint for cross-script adaptation of compact ASR models without the need for resource-intensive pre-training.
| Comments: | 5 pages, 2 figures. Accepted as a poster at the MusIML Workshop, ICML 2026 |
| Subjects: | Computation and Language (cs.CL) |
| ACM classes: | I.2.7; I.2.6 |
| Cite as: | arXiv:2607.09598 [cs.CL] |
| (or arXiv:2607.09598v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2607.09598 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Sanjid Hasan [view email]
[v1]
Fri, 10 Jul 2026 16:54:38 UTC (1,030 KB)
— Originally published at arxiv.org
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