基于众包数据构建卢干达语（Luganda）文本转语音模型
Building a Luganda Text-to-Speech Model From Crowdsourced Data
AI总结：
针对卢干达语TTS高质量单说话人数据稀缺的问题，通过筛选语调相近的多说话人录音并结合降噪、MOS筛选等预处理，显著提升了众包数据训练的TTS模型语音质量。
摘要：
针对卢干达语（Luganda）等非洲语言的文本转语音（TTS）技术发展仍较为有限，主要原因是训练TTS模型所需的高质量单说话人录音十分稀缺。此前的研究主要使用来自Common Voice数据集的多说话人卢干达语录音，这些说话人年龄在20至49岁之间。尽管生成的语音具备可懂度，但其质量仍低于基于专业录音室级录音训练的模型，这是因为用于提升Common Voice录音质量的数据预处理方法存在不足。此外，由于语调差异以及背景噪声的存在，语音收敛更难实现。\n本文提出，除了对训练数据进行更深入的预处理外，使用语调相近的多说话人数据进行训练，能够提升基于Common Voice的卢干达语TTS质量。具体而言，我们通过主观聆听对比语音录音，筛选出6名语调相近的女性说话人。除了裁剪录音首尾的静音片段外，我们还使用预训练的语音增强模型来降低背景噪声、提升音频质量。此外，我们利用预训练的非侵入式自监督平均意见得分（MOS）估计模型，筛选出估计MOS值高于3.5（即感知质量较高）的录音。\n9名卢干达语母语者的主观MOS评估结果显示，本文的TTS模型MOS值达到3.55，显著优于现有模型已报道的2.5的MOS值。此外，为保证公平对比，我们基于6名说话人训练的模型表现优于单说话人（MOS 3.13）和双说话人（MOS 3.22）训练的模型。这一结果证明，使用语调相近的多说话人数据弥补单说话人数据不足的方法，能够有效提升TTS质量。
英文摘要：
Text-to-speech (TTS) development for African languages such as Luganda is still limited, primarily due to the scarcity of high-quality, single-speaker recordings essential for training TTS models. Prior work has focused on utilizing the Luganda Common Voice recordings of multiple speakers aged between 20-49. Although the generated speech is intelligible, it is still of lower quality than the model trained on studio-grade recordings. This is due to the insufficient data preprocessing methods applied to improve the quality of the Common Voice recordings. Furthermore, speech convergence is more difficult to achieve due to varying intonations, as well as background noise. In this paper, we show that the quality of Luganda TTS from Common Voice can improve by training on multiple speakers of close intonation in addition to further preprocessing of the training data. Specifically, we selected six female speakers with close intonation determined by subjectively listening and comparing their voice recordings. In addition to trimming out silent portions from the beginning and end of the recordings, we applied a pre-trained speech enhancement model to reduce background noise and enhance audio quality. We also utilized a pre-trained, non-intrusive, self-supervised Mean Opinion Score (MOS) estimation model to filter recordings with an estimated MOS over 3.5, indicating high perceived quality. Subjective MOS evaluations from nine native Luganda speakers demonstrate that our TTS model achieves a significantly better MOS of 3.55 compared to the reported 2.5 MOS of the existing model. Moreover, for a fair comparison, our model trained on six speakers outperforms models trained on a single-speaker (3.13 MOS) or two speakers (3.22 MOS). This showcases the effectiveness of compensating for the lack of data from one speaker with data from multiple speakers of close intonation to improve TTS quality.
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。
