# Factors affecting ASR performance: A study using state of the art ASR models in Indic Languages

arXiv: https://arxiv.org/abs/2606.09335 · HTML: https://mlsanity.com/paper/2606.09335/

## 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 12 of its 27 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, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language](https://mlsanity.com/paper/2202.03555/) (2022)
- [FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech](https://mlsanity.com/paper/2205.12446/) (2022)
- [Massively Multilingual ASR: 50 Languages, 1 Model, 1 Billion Parameters](https://mlsanity.com/paper/2007.03001/) (2020)
- [VoiceFixer: Toward General Speech Restoration with Neural Vocoder](https://mlsanity.com/paper/2109.13731/) (2021)
- [IndicSUPERB: A Speech Processing Universal Performance Benchmark for Indian languages](https://mlsanity.com/paper/2208.11761/) (2022)
- [AudioSR: Versatile Audio Super-resolution at Scale](https://mlsanity.com/paper/2309.07314/) (2023)
- [CLSRIL-23: Cross Lingual Speech Representations for Indic Languages](https://mlsanity.com/paper/2107.07402/) (2021)
- [VAANI: Capturing the language landscape for an inclusive digital India](https://mlsanity.com/paper/2603.28714/) (2026)
- [data2vec-aqc: Search for the right Teaching Assistant in the Teacher-Student training setup](https://mlsanity.com/paper/2211.01246/) (2022)

## Abstract (arXiv)

ASR performance varies across languages, speakers, and recording conditions, yet systematic analysis for Indic languages remain limited. We present a large-scale study of decoded outputs from multiple open-source ASR models evaluated on diverse Indian speech datasets in zero-shot settings. We analyze linguistic, speaker-level, and acoustic factors across Hindi, Bengali, Kannada, Telugu, and Marathi. We examine correlations between WER and speaker traits such as average word length, speaking rate, and utterance duration across multiple model dataset pairs. For Hindi, we further analyze audio factors including telephone codecs, bit depth, resampling, and background noise. Results reveal both cross lingual patterns and language-specific sensitivities, showing how speaker behavior and signal processing choices affect ASR robustness in real world Indic scenarios.