38 citations · 76 across the 5 of their papers we have counts for
5 papers
Pretraining Techniques for Sequence-to-Sequence Voice Conversion
Wen-Chin Huang, Tomoki Hayashi, Yi-Chiao Wu +2
Sequence-to-sequence (seq2seq) voice conversion (VC) models are attractive owing to their ability to convert prosody. Nonetheless, without sufficient data, seq2seq VC models can su…
DiscreTalk: Text-to-Speech as a Machine Translation Problem
Tomoki Hayashi, Shinji Watanabe
This paper proposes a new end-to-end text-to-speech (E2E-TTS) model based on neural machine translation (NMT). The proposed model consists of two components; a non-autoregressive v…
Quasi-Periodic Parallel WaveGAN Vocoder: A Non-autoregressive Pitch-dependent Dilated Convolution Model for Parametric Speech Generation
Yi-Chiao Wu, Tomoki Hayashi, Takuma Okamoto +2
In this paper, we propose a parallel WaveGAN (PWG)-like neural vocoder with a quasi-periodic (QP) architecture to improve the pitch controllability of PWG. PWG is a compact non-aut…
Voice Transformer Network: Sequence-to-Sequence Voice Conversion Using Transformer with Text-to-Speech Pretraining
Wen-Chin Huang, Tomoki Hayashi, Yi-Chiao Wu +2
We introduce a novel sequence-to-sequence (seq2seq) voice conversion (VC) model based on the Transformer architecture with text-to-speech (TTS) pretraining. Seq2seq VC models are a…
ESPnet-TTS: Unified, Reproducible, and Integratable Open Source End-to-End Text-to-Speech Toolkit
Tomoki Hayashi, Ryuichi Yamamoto, Katsuki Inoue +6
This paper introduces a new end-to-end text-to-speech (E2E-TTS) toolkit named ESPnet-TTS, which is an extension of the open-source speech processing toolkit ESPnet. The toolkit sup…