Text-To-Speech Synthesis In The Wild

Jee-weon Jung, Wangyou Zhang, Soumi Maiti, Yihan Wu, Xin Wang +9 more
9/13/2024
eess.AScs.AI

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Abstract

Traditional Text-to-Speech (TTS) systems rely on studio-quality speech recorded in controlled settings.a Recently, an effort known as noisy-TTS training has emerged, aiming to utilize in-the-wild data. However, the lack of dedicated datasets has been a significant limitation. We introduce the TTS In the Wild (TITW) dataset, which is publicly available, created through a fully automated pipeline applied to the VoxCeleb1 dataset. It comprises two training sets: TITW-Hard, derived from the transcription, segmentation, and selection of raw VoxCeleb1 data, and TITW-Easy, which incorporates additional enhancement and data selection based on DNSMOS. State-of-the-art TTS models achieve over 3.0 UTMOS score with TITW-Easy, while TITW-Hard remains difficult showing UTMOS below 2.8.

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Cite this paper

@article{jung2024texttospeech,
  title  = {Text-To-Speech Synthesis In The Wild},
  author = {Jee-weon Jung and Wangyou Zhang and Soumi Maiti and Yihan Wu and Xin Wang and Ji-Hoon Kim and Yuta Matsunaga and Seyun Um and Jinchuan Tian and Hye-jin Shim and Nicholas Evans and Joon Son Chung and Shinnosuke Takamichi and Shinji Watanabe},
  year   = {2024},
  eprint = {2409.08711},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2409.08711v2}
}

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