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 \textit{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.

提出了 AccentFold 方法，通过利用学习到的口音嵌入之间的空间关系来提高自动语音识别（ASR）的性能，特别是在非洲口音的背景下。发现了 100 多种非洲口音之间的空间关系，强调了地理和谱系的相似性以及从语音中学到的一致的语音和形态学规律。通过实证评估，证明了 AccentFold 的有效性，对于未知分布的口音，基于 AccentFold 信息的训练样本提取优于强基准方法，相对字错误率（WER）改善了 4.6%。这一研究强调利用语言学关系来改善零样本 ASR 适应目标口音的潜力。