Phonologically-aware Automatic Speech Recognition Evaluation of Low-Resource Languages: The Case of Basque Dialects
Year: 2026
Type: conference-paper
Abstract: Automatic speech recognition models are typically trained with data of standard languages.However, their performance degrades when dealing with non-standard dialectal speech.In this paper, we present the first evaluation of an automatic speech recognition system for Basque, a low-resource language, based on spontaneous broadcast speech with high representation of dialectal speech.It relies on a 140-h manually annotated proprietary corpus of television programs in Basque, including dialect-level labels, as well as standardized and pseudo-phonetic transcriptions.We find that recognition performance significantly degrades for dialectal compared to standard speech, for all dialects present in our corpus.Subsequently, we provide a quantitative analysis of phonological phenomena based on single-word substitution errors, and identify 52 recurrent phenomena, grouped into sound deletions, epentheses, and substitutions.We further show a modest but statistically significant correlation between the number of phonological phenomena in an utterance and its recognition error rate.Our findings highlight the limitations of dialect-agnostic evaluation and motivate linguistically informed, dialect-aware strategies for automatic speech recognition in low-resource and typologically diverse languages.
Institution: University of the Basque Country
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166564476