Multi-Level Embedding Conformer Framework for Bengali Automatic Speech Recognition

Md. Nazmus Sakib; Golam Mahmud; Md. Maruf Bangabashi; Umme Ara Mahinur Istia; Md. Jahidul Islam; Partha Sarker; Afra Yeamini Prity

arXiv · 2601.09710

Multi-Level Embedding Conformer Framework for Bengali Automatic Speech Recognition

Md. Nazmus Sakib·Golam Mahmud·Md. Maruf Bangabashi·Umme Ara Mahinur Istia·Md. Jahidul Islam·Partha Sarker·Afra Yeamini Prity

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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.

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Md. Nazmus Sakib, Golam Mahmud, Md. Maruf Bangabashi, Umme Ara Mahinur Istia, Md. Jahidul Islam, Partha Sarker, Afra Yeamini Prity. 2025-12-23. Multi-Level Embedding Conformer Framework for Bengali Automatic Speech Recognition. https://arxiv.org/abs/2601.09710

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