Bengali ASR models fail due to English tokenizer design, transplantation fixes collapse
Lightweight speech recognition models optimized for edge deployment systematically fail on morphologically complex languages like Bengali due to English-centric tokenization schemes. Researchers identified that byte-level vocabularies fragment non-Latin scripts into excessive token chains, destabilizing autoregressive decoding. A vocabulary transplantation approach swaps the decoder's token space for a native-script WordPiece vocabulary, reducing token fertility by 86% and stabilizing inference. This work exposes a critical blind spot in model optimization: efficiency gains on Latin-script benchmarks often come at the cost of catastrophic failure on underrepresented languages, and suggests that tokenizer design must precede architecture optimization for truly multilingual edge deployment.
Modelwire context
ExplainerThe paper's core insight isn't just that Bengali ASR fails on edge models, but that the failure is *systematic and preventable at design time*. Standard efficiency optimization (byte-level tokenization for compression) actively breaks morphologically complex scripts, yet this trade-off is invisible in English benchmarks where byte-level vocabularies remain compact.
This connects directly to the sign language translation work from earlier this week, which also identified a critical accessibility gap in edge deployment. Both papers treat underrepresented modalities (non-Latin scripts, sign language) as first-class deployment constraints rather than post-hoc fixes. The Bengali ASR work goes further by naming the specific architectural choice (tokenizer selection) that must precede optimization, whereas the sign language pipeline solved the problem through hardware-aware compute splitting. Together they suggest a pattern: accessibility failures in edge AI aren't accidents but consequences of optimization priorities set during early design phases.
If the same 86% token reduction holds when tested on other morphologically complex languages (Tamil, Gujarati, Arabic), that confirms tokenizer transplantation is a general technique. If major edge ASR vendors (Moonshine, Whisper-derived models) adopt WordPiece vocabularies for non-Latin scripts within the next 12 months, the work has moved from research to practice; silence suggests the finding remains confined to the paper.
Coverage we drew on
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MentionsMoonshine · BanglaBERT · Bengali · WordPiece
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Modelwire summarizes, we don’t republish. arXiv cs.CL originally reported this story as “Tokenizer Transplantation: Mitigating Autoregressive Collapse in Edge-Efficient Bengali ASR”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.