面向非洲语言自动语音识别的合成语音数据
Synthetic Voice Data for Automatic Speech Recognition in African Languages
AI总结：
本文首次系统评估大规模合成语音语料库用于非洲语言ASR，通过LLM生成文本、TTS合成语音和ASR微调三步流程，以不到真实数据1%的成本创建超2500小时数据，并在豪萨语、杜鲁欧语和奇切瓦语上验证了合成数据对ASR性能的提升效果。
摘要：
语音技术对非洲2300多种语言中的大多数而言仍然难以触及。我们首次系统评估了面向非洲ASR的大规模合成语音语料库。我们采用三步流程：基于LLM的文本生成、TTS语音合成和ASR微调。在我们为其生成合成文本的十种语言中，有八种的可读性得分超过7分制中的5分。我们评估了三种语言（豪萨语、杜鲁欧语、奇切瓦语）的ASR改进情况，并以不到真实数据1%的成本创建了超过2500小时的合成语音数据。在250小时真实数据和250小时合成豪萨语数据上微调的Wav2Vec-BERT-2.0模型与仅使用500小时真实数据的基线模型表现相当，而579小时真实数据与450至993小时合成数据组合则取得了最佳性能。我们还呈现了按性别分列的ASR性能评估。对于资源极少的语言，增益各不相同：奇切瓦语在真实与合成数据比例为1:2时，WER相对改善约6.5%；杜鲁欧语在1:1比例下，在某些评估数据上显示出类似改进，但在其他数据上则没有。对编码者间信度、ASR错误和评估数据集的调查表明，需要更稳健的评审协议和更准确的评估数据。所有数据和模型均已公开发布，以邀请进一步研究改进面向非洲语言的合成数据。
英文摘要：
Speech technology remains out of reach for most of the over 2300 languages in Africa. We present the first systematic assessment of large-scale synthetic voice corpora for African ASR. We apply a three-step process: LLM-driven text creation, TTS voice synthesis, and ASR fine-tuning. Eight out of ten languages for which we create synthetic text achieved readability scores above 5 out of 7. We evaluated ASR improvement for three (Hausa, Dholuo, Chichewa) and created more than 2,500 hours of synthetic voice data at below 1% of the cost of real data. Fine-tuned Wav2Vec-BERT-2.0 models trained on 250h real and 250h synthetic Hausa matched a 500h real-data-only baseline, while 579h real and 450h to 993h synthetic data created the best performance. We also present gender-disaggregated ASR performance evaluation. For very low-resource languages, gains varied: Chichewa WER improved about 6.5% relative with a 1:2 real-to-synthetic ratio; a 1:1 ratio for Dholuo showed similar improvements on some evaluation data, but not on others. Investigating intercoder reliability, ASR errors and evaluation datasets revealed the need for more robust reviewer protocols and more accurate evaluation data. All data and models are publicly released to invite further work to improve synthetic data 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。
