New Study Questions Linguistic Relatedness as Key to Low-Resource African Language ASR

Research into automatic speech recognition (ASR) for African languages, particularly those with limited data resources, often explores methods to reduce the need for extensive target-language data. One such approach involves leveraging linguistic relatedness, where a model is first adapted using a language similar to the target language, theoretically reducing the data required for the target. While this strategy has shown promise in text-based models, its efficacy in the speech domain, especially for large multilingual ASR systems, has been a subject of debate.

A recent study investigated this hypothesis using a rigorous experimental design. It incorporated two African-centric speech corpora and four distinct large ASR models, systematically evaluating the impact of pre-adaptation on linguistically related auxiliary languages. The core aim was to determine if linguistic relatedness reliably predicts improvements in cross-lingual transfer, thereby facilitating the extension of ASR to low-resource languages.

The findings indicate that, across all tested conditions, pre-adaptation using related auxiliary languages did not result in significant practical improvements once even a minimal amount (as little as one hour) of target-language data was made available. This suggests that linguistic relatedness alone might not be a reliable predictor of transfer gains in large multilingual ASR systems, nor an effective standalone strategy for expanding these models to languages with scarce data resources. This has important implications for how researchers approach developing ASR for Africa's diverse linguistic landscape.