TTS, or text-to-speech, is a complicated process that can be accomplished through appropriate modeling using deep learning methods. In order to implement deep learning models, a suitable dataset is required. Since there is a scarce amount of work done in this field for the Persian language, this paper will introduce the single speaker dataset: ArmanTTS. We compared the characteristics of this dataset with those of various prevalent datasets to prove that ArmanTTS meets the necessary standards for teaching a Persian text-to-speech conversion model. We also combined the Tacotron 2 and HiFi GAN to design a model that can receive phonemes as input, with the output being the corresponding speech. 4.0 value of MOS was obtained from real speech, 3.87 value was obtained by the vocoder prediction and 2.98 value was reached with the synthetic speech generated by the TTS model.
翻译:TTS(文本到语音)是一种复杂的过程,可以通过适当使用深度学习方法进行建模来完成。为了实现深度学习模型,需要合适的数据集。由于在波斯语中该领域的工作较少,因此本文介绍了单发音者数据集:ArmanTTS。我们比较了这个数据集的特征与多种流行数据集的特征,证明 ArmanTTS 符合教授波斯语文本到语音转换模型所需的标准。我们还结合了Tacotron 2和HiFi GAN来设计一个模型,该模型可以接收音素作为输入,输出对应的语音。由真实语音获得了4.0 的 MOS 值,由声码器预测获得了3.87 的值,通过 TTS 模型生成的合成语音获得了2.98 的值。