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    <abstract>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 associated with phylogenetic distance across 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.</abstract>
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%0 Conference Proceedings
%T Dialect Matters: Cross-Lingual ASR Transfer for Low-Resource Indic Language Varieties
%A Dhasmana, Akriti
%A Srivastava, Aarohi
%A Chiang, David
%S Proceedings of the 13th Workshop on NLP for Similar Languages, Varieties and Dialects
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%F dhasmana-etal-2026-dialect
%X 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 associated with phylogenetic distance across 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.
%U https://aclanthology.org/2026.vardial-1.12/
%P 145-156

Markdown (Informal)

[Dialect Matters: Cross-Lingual ASR Transfer for Low-Resource Indic Language Varieties](https://aclanthology.org/2026.vardial-1.12/) (Dhasmana et al., VarDial 2026)

• Dialect Matters: Cross-Lingual ASR Transfer for Low-Resource Indic Language Varieties (Dhasmana et al., VarDial 2026)

ACL

• Akriti Dhasmana, Aarohi Srivastava, and David Chiang. 2026. Dialect Matters: Cross-Lingual ASR Transfer for Low-Resource Indic Language Varieties. In Proceedings of the 13th Workshop on NLP for Similar Languages, Varieties and Dialects, pages 145–156, Rabat, Morocco. Association for Computational Linguistics.