Dialect Bias in Speech Recognition Across 10 Spanish and French Varieties

ASR bias research is largely English-focused and the mechanisms underlying performance disparities remain poorly understood. We address both gaps for Spanish and French. Using a 20-hour, gender-balanced corpus (the first for these languages designed for dialect bias analysis, with country-level stratification and manual transcripts preserving dialectal features), we benchmark seven models and find non-uniform disparities: models favor European varieties in French, while Spanish reveals unexpected patterns with Dominican outperforming Peninsular and with Chilean showing the highest error rates. Lexical analysis links failures to morphosyntactic "correction" and regional discourse markers; acoustic analysis shows that speaker embeddings encode dialect information; a diagnostic fine-tuning experiment localizes these errors to the decoder. Together, these results demonstrate that ASR errors systematically reflect linguistic distance from training distributions.

• Machine intelligence