A study on the impact of region specific data on the performance of Indic ASR
On the map: Speech and audio language models → Multilingual and low-resource ASR
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.
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.
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7 of 16 references.
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- 2024LAHAJA: A Robust Multi-accent Benchmark for Evaluating Hindi ASR Systems21 Aug
- 2022IndicSUPERB: A Speech Processing Universal Performance Benchmark for Indian languages24 Aug
Other references · 5
- 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,661
- 2020Unsupervised Cross-lingual Representation Learning for Speech Recognitioncited by 554
- 2021MuRIL: Multilingual Representations for Indian Languagescited by 185
- 2026VAANI: Capturing the language landscape for an inclusive digital Indiacited by 14
- 2020Word Error Rate Estimation Without ASR Output: e-WER2cited by 9
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- 2021Multilingual and code-switching ASR challenges for low resource Indian languages