Dialect Matters: Cross-Lingual ASR Transfer for Low-Resource Indic Language Varieties
This addresses challenges in ASR for low-resource dialectal speech, but it is incremental as it builds on existing cross-lingual transfer methods.
The study tackled the problem of cross-lingual transfer for low-resource Indic dialects in ASR, finding that fine-tuning on small dialectal data often matches performance from larger high-resource languages, and analyzing transcription errors revealed biases toward pre-training languages.
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.