NaijaVoices数据集：为非洲语言培育大规模、高质量、文化丰富的语音数据
The NaijaVoices Dataset: Cultivating Large-Scale, High-Quality, Culturally-Rich Speech Data for African Languages
AI总结：
针对非洲语音技术数据不足问题，构建了包含1800小时、5000多名说话人的NaijaVoices数据集，通过微调实验显著提升了ASR性能。
摘要：
高性能、鲁棒且可靠的语音技术的发展依赖于大规模、高质量的数据集。然而，非洲语言（包括我们关注的Igbo、Hausa和Yoruba）由于数据不足而代表性不足。流行的语音技术不支持2000多种非洲语言中的任何一种，限制了约十亿人的可访问性。虽然目标语言存在先前的数据集工作，但它们缺乏鲁棒语音模型所需的规模和多样性。为了弥补这一差距，我们引入了NaijaVoices数据集，这是一个包含5000多名说话人的1800小时语音-文本数据集。我们概述了独特的数据收集方法，分析了其声学多样性，并通过自动语音识别的微调实验展示了其影响，平均实现了75.86%（Whisper）、52.06%（MMS）和42.33%（XLSR）的WER改进。这些结果突显了NaijaVoices在推进非洲语言多语言语音处理方面的潜力。
英文摘要：
The development of high-performing, robust, and reliable speech technologies depends on large, high-quality datasets. However, African languages -- including our focus, Igbo, Hausa, and Yoruba -- remain under-represented due to insufficient data. Popular voice-enabled technologies do not support any of the 2000+ African languages, limiting accessibility for circa one billion people. While previous dataset efforts exist for the target languages, they lack the scale and diversity needed for robust speech models. To bridge this gap, we introduce the NaijaVoices dataset, a 1,800-hour speech-text dataset with 5,000+ speakers. We outline our unique data collection approach, analyze its acoustic diversity, and demonstrate its impact through finetuning experiments on automatic speech recognition, averagely achieving 75.86% (Whisper), 52.06% (MMS), and 42.33% (XLSR) WER improvements. These results highlight NaijaVoices' potential to advance multilingual speech processing for African languages.
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。
