BanglaBERT

Tokenizer transplantation fixes autoregressive collapse in Bengali edge ASR

Researchers identify that English-centric byte-level tokenizers cause catastrophic autoregressive collapse when lightweight ASR models like Moonshine are applied to morphologically rich languages like Bengali. They propose a vocabulary transplantation pipeline that swaps in BanglaBERT's WordPiece vocabulary, reducing token fertility from 9.16 to 1.30 and cutting autoregressive sequence length by 85.8%. The modified model achieves 21.54% WER and an RTF of 0.0053 on the 882-hour Lipi-Ghor dataset, offering a reproducible blueprint for cross-script adaptation of compact ASR models without retraining from scratch.