# Model Adaptation for ASR in low-resource Indian Languages

arXiv: https://arxiv.org/abs/2307.07948 · HTML: https://mlsanity.com/paper/2307.07948/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 16 Jul 2023.

## Builds on (most cited first, 5 of 5)

- [Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages](https://mlsanity.com/paper/2104.05596/) (2021)
- [Not Low-Resource Anymore: Aligner Ensembling, Batch Filtering, and New Datasets for Bengali-English Machine Translation](https://mlsanity.com/paper/2009.09359/) (2020)
- [Classification Benchmarks for Under-resourced Bengali Language based on Multichannel Convolutional-LSTM Network](https://mlsanity.com/paper/2004.07807/) (2020)
- [Annotated Speech Corpus for Low Resource Indian Languages: Awadhi, Bhojpuri, Braj and Magahi](https://mlsanity.com/paper/2206.12931/) (2022)
- [Challenges and Opportunities of Speech Recognition for Bengali Language](https://mlsanity.com/paper/2109.13217/) (2021)

## Built on by (most cited first, 4 of 4)

- [Adaptability of ASR Models on Low-Resource Language: A Comparative Study of Whisper and Wav2Vec-BERT on Bangla](https://mlsanity.com/paper/2507.01931/) (2025)
- [Dialect Adaptation and Data Augmentation for Low-Resource ASR: TalTech Systems for the MADASR 2023 Challenge](https://mlsanity.com/paper/2310.17448/) (2023)
- [Transsion TSUP's speech recognition system for ASRU 2023 MADASR Challenge](https://mlsanity.com/paper/2307.11778/) (2023)
- [Robust Assamese Speech Recognition through Controlled Fine-Tuning of Whisper Models](https://mlsanity.com/paper/2607.17164/) (2026)

## Abstract (arXiv)

Automatic speech recognition (ASR) performance has improved drastically in recent years, mainly enabled by self-supervised learning (SSL) based acoustic models such as wav2vec2 and large-scale multi-lingual training like Whisper. A huge challenge still exists for low-resource languages where the availability of both audio and text is limited. This is further complicated by the presence of multiple dialects like in Indian languages. However, many Indian languages can be grouped into the same families and share the same script and grammatical structure. This is where a lot of adaptation and fine-tuning techniques can be applied to overcome the low-resource nature of the data by utilising well-resourced similar languages. In such scenarios, it is important to understand the extent to which each modality, like acoustics and text, is important in building a reliable ASR. It could be the case that an abundance of acoustic data in a language reduces the need for large text-only corpora. Or, due to the availability of various pretrained acoustic models, the vice-versa could also be true. In this proposed special session, we encourage the community to explore these ideas with the data in two low-resource Indian languages of Bengali and Bhojpuri. These approaches are not limited to Indian languages, the solutions are potentially applicable to various languages spoken around the world.