# Bangla-Wave: Improving Bangla Automatic Speech Recognition Utilizing N-gram Language Models

arXiv: https://arxiv.org/abs/2209.12650 · HTML: https://mlsanity.com/paper/2209.12650/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 13 references that are in the map.
- First posted on arXiv 13 Sep 2022.

## Builds on (most cited first, 8 of 8)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://mlsanity.com/paper/2012.03411/) (2020)
- [VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://mlsanity.com/paper/2101.00390/) (2021)
- [BABEL: Bodies, Action and Behavior with English Labels](https://mlsanity.com/paper/2106.09696/) (2021)
- [VoxLingua107: a Dataset for Spoken Language Recognition](https://mlsanity.com/paper/2011.12998/) (2020)
- [A Large Multi-Target Dataset of Common Bengali Handwritten Graphemes](https://mlsanity.com/paper/2010.00170/) (2020)
- [Bengali Common Voice Speech Dataset for Automatic Speech Recognition](https://mlsanity.com/paper/2206.14053/) (2022)

## Built on by (most cited first, 3 of 3)

- [Pseudo-Labeling for Domain-Agnostic Bangla Automatic Speech Recognition](https://mlsanity.com/paper/2311.03196/) (2023)
- [BanglaTalk: Towards Real-Time Speech Assistance for Bengali Regional Dialects](https://mlsanity.com/paper/2510.06188/) (2025)
- [Towards Linguistically-Aware and Language-Independent Tokenization for Large Language Models (LLMs)](https://mlsanity.com/paper/2410.03568/) (2024)

## Abstract (arXiv)

Although over 300M around the world speak Bangla, scant work has been done in improving Bangla voice-to-text transcription due to Bangla being a low-resource language. However, with the introduction of the Bengali Common Voice 9.0 speech dataset, Automatic Speech Recognition (ASR) models can now be significantly improved. With 399hrs of speech recordings, Bengali Common Voice is the largest and most diversified open-source Bengali speech corpus in the world. In this paper, we outperform the SOTA pretrained Bengali ASR models by finetuning a pretrained wav2vec2 model on the common voice dataset. We also demonstrate how to significantly improve the performance of an ASR model by adding an n-gram language model as a post-processor. Finally, we do some experiments and hyperparameter tuning to generate a robust Bangla ASR model that is better than the existing ASR models.