788 citations · 1.2k across the 41 of their papers we have counts for
12 papers · 1 filter
The 2020 ESPnet update: new features, broadened applications, performance improvements, and future plans
Shinji Watanabe, Florian Boyer, Xuankai Chang +12
This paper describes the recent development of ESPnet (https://github.com/espnet/espnet), an end-to-end speech processing toolkit. This project was initiated in December 2017 to ma…
ESPnet-se: end-to-end speech enhancement and separation toolkit designed for asr integration
Chenda Li, Jing Shi, Wangyou Zhang +8
We present ESPnet-SE, which is designed for the quick development of speech enhancement and speech separation systems in a single framework, along with the optional downstream spee…
Recent Developments on ESPnet Toolkit Boosted by Conformer
Pengcheng Guo, Florian Boyer, Xuankai Chang +12
In this study, we present recent developments on ESPnet: End-to-End Speech Processing toolkit, which mainly involves a recently proposed architecture called Conformer, Convolution-…
Any-to-One Sequence-to-Sequence Voice Conversion using Self-Supervised Discrete Speech Representations
Wen-Chin Huang, Yi-Chiao Wu, Tomoki Hayashi +1
We present a novel approach to any-to-one (A2O) voice conversion (VC) in a sequence-to-sequence (seq2seq) framework. A2O VC aims to convert any speaker, including those unseen duri…
The Sequence-to-Sequence Baseline for the Voice Conversion Challenge 2020: Cascading ASR and TTS
Wen-Chin Huang, Tomoki Hayashi, Shinji Watanabe +1
This paper presents the sequence-to-sequence (seq2seq) baseline system for the voice conversion challenge (VCC) 2020. We consider a naive approach for voice conversion (VC), which…
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…
Quasi-Periodic Parallel WaveGAN: A Non-autoregressive Raw Waveform Generative Model with Pitch-dependent Dilated Convolution Neural Network
Yi-Chiao Wu, Tomoki Hayashi, Takuma Okamoto +2
In this paper, we propose a quasi-periodic parallel WaveGAN (QPPWG) waveform generative model, which applies a quasi-periodic (QP) structure to a parallel WaveGAN (PWG) model using…
Quasi-Periodic WaveNet: An Autoregressive Raw Waveform Generative Model with Pitch-dependent Dilated Convolution Neural Network
Yi-Chiao Wu, Tomoki Hayashi, Patrick Lumban Tobing +2
In this paper, a pitch-adaptive waveform generative model named Quasi-Periodic WaveNet (QPNet) is proposed to improve the limited pitch controllability of vanilla WaveNet (WN) usin…
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…
ESPnet-ST: All-in-One Speech Translation Toolkit
Hirofumi Inaguma, Shun Kiyono, Kevin Duh +4
We present ESPnet-ST, which is designed for the quick development of speech-to-speech translation systems in a single framework. ESPnet-ST is a new project inside end-to-end speech…
End-to-End Automatic Speech Recognition Integrated With CTC-Based Voice Activity Detection
Takenori Yoshimura, Tomoki Hayashi, Kazuya Takeda +1
This paper integrates a voice activity detection (VAD) function with end-to-end automatic speech recognition toward an online speech interface and transcribing very long audio reco…