  Despite advancements in speech recognition, accented speech remains
challenging. While previous approaches have focused on modeling techniques or
creating accented speech datasets, gathering sufficient data for the multitude
of accents, particularly in the African context, remains impractical due to
their sheer diversity and associated budget constraints. To address these
challenges, we propose AccentFold, a method that exploits spatial relationships
between learned accent embeddings to improve downstream Automatic Speech
Recognition (ASR). Our exploratory analysis of speech embeddings representing
100+ African accents reveals interesting spatial accent relationships
highlighting geographic and genealogical similarities, capturing consistent
phonological, and morphological regularities, all learned empirically from
speech. Furthermore, we discover accent relationships previously
uncharacterized by the Ethnologue. Through empirical evaluation, we demonstrate
the effectiveness of AccentFold by showing that, for out-of-distribution (OOD)
accents, sampling accent subsets for training based on AccentFold information
outperforms strong baselines a relative WER improvement of 4.6%. AccentFold
presents a promising approach for improving ASR performance on accented speech,
particularly in the context of African accents, where data scarcity and budget
constraints pose significant challenges. Our findings emphasize the potential
of leveraging linguistic relationships to improve zero-shot ASR adaptation to
target accents.
