# Dialect Matters: Cross-Lingual ASR Transfer for Low-Resource Indic Language Varieties

arXiv: https://arxiv.org/abs/2601.04373 · HTML: https://mlsanity.com/paper/2601.04373/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 7 Jan 2026.

## Builds on (most cited first, 10 of 10)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [W2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training](https://mlsanity.com/paper/2108.06209/) (2021)
- [Multilingual and code-switching ASR challenges for low resource Indian languages](https://mlsanity.com/paper/2104.00235/) (2021)
- [Quantifying the Dialect Gap and its Correlates Across Languages](https://mlsanity.com/paper/2310.15135/) (2023)
- [Towards Building Text-To-Speech Systems for the Next Billion Users](https://mlsanity.com/paper/2211.09536/) (2022)
- [Vakyansh: ASR Toolkit for Low Resource Indic languages](https://mlsanity.com/paper/2203.16512/) (2022)
- [Language Complexity and Speech Recognition Accuracy: Orthographic Complexity Hurts, Phonological Complexity Doesn't](https://mlsanity.com/paper/2406.09202/) (2024)

## Built on by (most cited first, 2 of 2)

- [Seeds Before Objectives: Rethinking Evaluation for Low-Resource Garhwali ASR](https://mlsanity.com/paper/2608.10670/) (2026)
- [From Sinhala to Dhivehi: Cross-Lingual Transfer Learning for Low-Resource Speech Recognition](https://mlsanity.com/paper/2607.06289/) (2026)

## Abstract (arXiv)

We conduct an empirical study of cross-lingual transfer using spontaneous, noisy, and code-mixed speech across a wide range of Indic dialects and language varieties. Our results indicate that although ASR performance is generally improved with reduced phylogenetic distance between languages, this factor alone does not fully explain performance in dialectal settings. Often, fine-tuning on smaller amounts of dialectal data yields performance comparable to fine-tuning on larger amounts of phylogenetically-related, high-resource standardized languages. We also present a case study on Garhwali, a low-resource Pahari language variety, and evaluate multiple contemporary ASR models. Finally, we analyze transcription errors to examine bias toward pre-training languages, providing additional insight into challenges faced by ASR systems on dialectal and non-standardized speech.