KazakhTTS：一个开源的哈萨克语文本转语音合成数据集
KazakhTTS: An Open-Source Kazakh Text-to-Speech Synthesis Dataset
摘要：
本文介绍了一个高质量的哈萨克语开源语音合成数据集，哈萨克语是一种全球超过1300万人使用的低资源语言。该数据集由两名专业演讲者（女性和男性）讲的大约93小时的转录音频记录组成。这是第一个公开的大规模数据集，旨在促进学术界和工业界的哈萨克语文本转语音（TTS）应用。在本文中，我们通过描述数据集开发过程和面临的挑战来分享我们的经验，并讨论未来的重要方向。为了证明我们数据集的可靠性，我们建立了基线端到端TTS模型，并使用主观平均意见评分（MOS）指标对其进行评估。评估结果表明，在我们的数据集上训练的最佳TTS模型对于两个说话者都达到了4以上的MOS，这使得它们适用于实际使用。数据集、训练配方和预训练的TTS模型都是免费提供的。
英文摘要：
This paper introduces a high-quality open-source speech synthesis dataset for Kazakh, a low-resource language spoken by over 13 million people worldwide. The dataset consists of about 93 hours of transcribed audio recordings spoken by two professional speakers (female and male). It is the first publicly available large-scale dataset developed to promote Kazakh text-to-speech (TTS) applications in both academia and industry. In this paper, we share our experience by describing the dataset development procedures and faced challenges, and discuss important future directions. To demonstrate the reliability of our dataset, we built baseline end-to-end TTS models and evaluated them using the subjective mean opinion score (MOS) measure. Evaluation results show that the best TTS models trained on our dataset achieve MOS above 4 for both speakers, which makes them applicable for practical use. The dataset, training recipe, and pretrained TTS models are freely available.
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。
