# A study on the impact of region specific data on the performance of Indic ASR

arXiv: https://arxiv.org/abs/2606.09345 · HTML: https://mlsanity.com/paper/2606.09345/

## Key facts

- No citing papers in the map yet (data through 19 Oct 2026).
- Placed in Multilingual and low-resource ASR, one of 11 directions in Speech and audio language models.
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 8 Jun 2026.

## Placed in

- Speech and audio language models / [Multilingual and low-resource ASR](https://mlsanity.com/speech/multilingual-and-low-resource-asr/)

## Builds on (most cited first, 7 of 7)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [MuRIL: Multilingual Representations for Indian Languages](https://mlsanity.com/paper/2103.10730/) (2021)
- [IndicSUPERB: A Speech Processing Universal Performance Benchmark for Indian languages](https://mlsanity.com/paper/2208.11761/) (2022)
- [VAANI: Capturing the language landscape for an inclusive digital India](https://mlsanity.com/paper/2603.28714/) (2026)
- [LAHAJA: A Robust Multi-accent Benchmark for Evaluating Hindi ASR Systems](https://mlsanity.com/paper/2408.11440/) (2024)
- [Word Error Rate Estimation Without ASR Output: e-WER2](https://mlsanity.com/paper/2008.03403/) (2020)

## Abstract (arXiv)

Automatic Speech Recognition (ASR) systems are widely deployed across linguistically diverse regions, yet their ability to generalize across fine-grained geographic variation remains underexplored. We present a systematic study of cross-district ASR generalization for Indian languages, analyzing the impact of regional variation on performance. Using finetuning as a controlled probe, we train models on speech from a single district and evaluate them on other districts within the same language. We examine trends across multiple train test district pairs and quantify performance differences. To assess geographic effects, we analyze the correlation between WER and inter district distance using two distance measures. Our results show consistent correlations between geographic distance and WER, highlighting the challenges of regional generalization and the need for geographically diverse speech data in ASR development and evaluation in India.