Tokenizer Transplantation: Mitigating Autoregressive Collapse in Edge-Efficient Bengali ASR

Abstract: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.

5 pages, 2 figures. Accepted as a poster at the MusIML Workshop, ICML 2026

Computation and Language (cs.CL)

arXiv:2607.09598 [cs.CL]

(or arXiv:2607.09598v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.09598 Focus to learn more arXiv-issued DOI via DataCite

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