Multi-Level Embedding Conformer Framework for Bengali Automatic Speech Recognition
Abstract
Bengali, spoken by over 300 million people, is a morphologically rich and lowresource language, posing challenges for automatic speech recognition (ASR). This research presents an end-to-end framework for Bengali ASR, building on a Conformer-CTC backbone with a multi-level embedding fusion mechanism that incorporates phoneme, syllable, and wordpiece representations. By enriching acoustic features with these linguistic embeddings, the model captures fine-grained phonetic cues and higher-level contextual patterns. The architecture employs early and late Conformer stages, with preprocessing steps including silence trimming, resampling, Log-Mel spectrogram extraction, and SpecAugment augmentation. The experimental results demonstrate the strong potential of the model, achieving a word error rate (WER) of 10.01% and a character error rate (CER) of 5.03%. These results demonstrate the effectiveness of combining multi-granular linguistic information with acoustic modeling, providing a scalable approach for low-resource ASR development.
Cite
@article{arxiv.2601.09710,
title = {Multi-Level Embedding Conformer Framework for Bengali Automatic Speech Recognition},
author = {Md. Nazmus Sakib and Golam Mahmud and Md. Maruf Bangabashi and Umme Ara Mahinur Istia and Md. Jahidul Islam and Partha Sarker and Afra Yeamini Prity},
journal= {arXiv preprint arXiv:2601.09710},
year = {2026}
}