Thiomi数据集：一个大规模多模态语料库，用于低资源非洲语言
The Thiomi Dataset: A Large-Scale Multimodal Corpus for Low-Resource African Languages
AI总结：
Thiomi数据集包含超过60万条文本注释和38.5万条音频记录，用于训练和评估ASR、MT和TTS模型，显著提升了非洲语言的语音识别性能。
摘要：
我们介绍了Thiomi数据集，这是一个涵盖十种非洲语言（四个语系）的大型多模态语料库，包括超过601,000条批准的句子级文本注释和超过385,000条音频记录。数据集通过专门的社区数据收集平台收集，涉及超过100名贡献者。为了验证数据集的实用性，我们训练并评估了ASR、MT和TTS模型，建立了所有语言的基准。我们的最佳ASR系统在斯瓦希里语（Common Voice）上实现了3.24%的WER，将先前学术SOTA从8.3%降至3.24%（5.1个百分点绝对，61%的相对减少），并在索马里语上实现了4.3%的WER。数据集将发布在HuggingFace上。我们描述了收集平台、质量保证流程和基准实验，并讨论了对非洲语言技术基础设施的影响。
英文摘要：
We present the Thiomi Dataset, a large-scale multimodal corpus spanning ten African languages across four language families: Swahili, Kikuyu, Kamba, Kimeru, Luo, Maasai, Kipsigis, Somali (East Africa); Wolof (West Africa); and Fulani (West/Central Africa). The dataset contains over 601,000 approved sentence-level text annotations and over 385,000 audio recordings, collected through a dedicated community data collection platform involving over 100 contributors. To validate the dataset's utility, we train and evaluate ASR, MT, and TTS models, establishing baselines across all languages. Our best ASR system achieves 3.24% WER on Swahili (Common Voice), reducing prior academic SOTA from 8.3% to 3.24% (5.1 percentage point absolute, 61% relative reduction), and 4.3% WER on Somali. The dataset will be published on HuggingFace. We describe the collection platform, quality assurance workflows, and baseline experiments, and discuss implications for African language technology infrastructure.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
