In this work, we present AfriHuBERT, an extension of mHuBERT-147, a state-of-the-art (SOTA) and compact self-supervised learning (SSL) model, originally pretrained on 147 languages. While mHuBERT-147 was pretrained on 16 African languages, we expand this to cover 39 African languages through continued pretraining on 6,500+ hours of speech data aggregated from diverse sources, including 23 newly added languages. We evaluate AfriHuBERT on two key speech tasks: Language Identification (LID) and Automatic Speech Recognition (ASR) using FLEURS dataset. Our results show a +4% F1 score improvement on average for LID and a -1.2% average Word Error Rate (WER) reduction for ASR. Further analysis shows that ASR models trained on AfriHuBERT exhibit improved cross-corpus generalization. Additionally, the analysis indicates that the FLEURS have data quality limitations that may affect their suitability for evaluating low-resource African languages, suggesting the need for better evaluation benchmarks for these languages.

本研究提出了AfriHuBERT，一个基于mHuBERT-147的自监督学习模型，通过在6500小时的语音数据上继续预训练，将支持的非洲语言数量从16种扩展到39种。研究结果显示，其在语言识别和自动语音识别任务中的表现有所提升，并指出现有评估基准对于低资源非洲语言可能存在的数据质量限制，亟需改进。

AfriHuBERT：针对非洲语言的自监督语音表示模型