Summary
A paper by Sanjid Hasan and Md. Abdur Rahman (arXiv:2607.09598) addresses a key failure mode of lightweight ASR models on edge devices. Highly optimized architectures such as Moonshine perform poorly on morphologically rich, non-Latin languages like Bangla because their English-centric byte-level tokenizers fragment Bangla words into long byte chains with high token fertility, triggering catastrophic autoregressive collapse during decoding. The authors propose a vocabulary transplantation pipeline that replaces the decoder vocabulary with the native-script BanglaBERT WordPiece vocabulary and resizes the embedding matrix accordingly. This reduces token fertility from 9.16 to 1.30, cuts autoregressive sequence length by 85.8%, and fully eliminates decoding instability. Evaluated on the 882-hour Lipi-Ghor dataset, the system achieves a word error rate of 21.54% with a real-time factor of 0.0053, demonstrating that tokenizer adaptation is a practical route to multilingual edge ASR without sacrificing efficiency.
Paper Overview
- Field: NLP / Speech
- Authors: Sanjid Hasan, Md. Abdur Rahman
- Published: 2026-07-10
- arXiv: 2607.09598
Abstract
Lightweight speech recognition models are essential for edge deployment, but highly optimized architectures such as Moonshine often fail on morphologically rich, non-Latin languages like Bangla. The root cause is that English-centric byte-level tokenizers fragment Bangla words into long chains of bytes with high token fertility, which triggers catastrophic autoregressive collapse during decoding.
Proposed Method
The authors introduce a vocabulary transplantation pipeline:
- Replace the decoder vocabulary with the native-script BanglaBERT WordPiece vocabulary
- Resize the embedding matrix to match the new vocabulary
Results
- Token fertility drops from 9.16 to 1.30
- Autoregressive sequence length reduced by 85.8%
- Decoding instability is fully mitigated
- On the 882-hour Lipi-Ghor dataset:
- WER: 21.54%
- RTF: 0.0053
The work shows that simply transplanting a native-script tokenizer can unlock edge-optimized ASR architectures for non-Latin languages.
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