ArmanTTS single-speaker Persian dataset

Mohammd Hasan Shamgholi, Vahid Saeedi, Javad Peymanfard, Leila Alhabib, Hossein Zeinali
4/7/2023
cs.CL

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Abstract

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.

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Cite this paper

@article{shamgholi2023armantts,
  title  = {ArmanTTS single-speaker Persian dataset},
  author = {Mohammd Hasan Shamgholi and Vahid Saeedi and Javad Peymanfard and Leila Alhabib and Hossein Zeinali},
  year   = {2023},
  eprint = {2304.03585},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2304.03585v1}
}

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