Abstract
We present Ethio-ASR, a suite of multilingual CTC-based automatic speech recognition (ASR) models jointly trained on five Ethiopian languages: Amharic, Tigrinya, Oromo, Sidaama, and Wolaytta. These languages belong to the Semitic, Cushitic, and Omotic branches of the Afroasiatic family, and remain severely underrepresented in speech technology despite being spoken by the vast majority of Ethiopia's population. We train our models on the recently released WAXAL corpus using several pre-trained speech encoders and evaluate against strong multilingual baselines, including OmniASR. Our best model achieves an average WER of 30.48% on the WAXAL test set, outperforming the best OmniASR model with substantially fewer parameters. We further provide a comprehensive analysis of gender bias, the contribution of vowel length and consonant gemination to ASR errors, and the training dynamics of multilingual CTC models. Our models and codebase are publicly available to the research community.
Abstract (translated)
我们推出了Ethio-ASR，这是一套基于CTC的多语言自动语音识别模型，在埃塞俄比亚五种语言（阿姆哈拉语、提格雷语、奥罗莫语、西达马语和沃莱塔语）上进行了联合训练。这些语言属于亚非语系的闪米特语支、库希特语支和奥莫特语支，尽管被埃塞俄比亚绝大多数人口使用，但在语音技术领域仍严重缺乏代表性。我们基于近期发布的WAXAL语料库训练模型，采用了多种预训练语音编码器，并与OmniASR等强大多语言基线模型进行对比评估。最佳模型在WAXAL测试集上达到平均30.48%的词错误率，以显著更少的参数数量超越了最优的OmniASR模型。我们还进一步提供了关于性别偏差、元音长度与辅音重叠对识别错误的影响，以及多语言CTC模型训练动态的全面分析。我们的模型和代码库已向研究社区公开。
URL
https://arxiv.org/abs/2603.23654
