178 citations · 496 across the 34 of their papers we have counts for
11 papers · 1 filter
DisC-VC: Disentangled and F0-Controllable Neural Voice Conversion
Chihiro Watanabe, Hirokazu Kameoka
Voice conversion is a task to convert a non-linguistic feature of a given utterance. Since naturalness of speech strongly depends on its pitch pattern, in some applications, it wou…
X-DC: Explainable Deep Clustering based on Learnable Spectrogram Templates
Chihiro Watanabe, Hirokazu Kameoka
Deep neural networks (DNNs) have achieved substantial predictive performance in various speech processing tasks. Particularly, it has been shown that a monaural speech separation t…
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…
Nonparallel Voice Conversion with Augmented Classifier Star Generative Adversarial Networks
Hirokazu Kameoka, Takuhiro Kaneko, Kou Tanaka +1
We previously proposed a method that allows for nonparallel voice conversion (VC) by using a variant of generative adversarial networks (GANs) called StarGAN. The main features of…
Many-to-Many Voice Transformer Network
Hirokazu Kameoka, Wen-Chin Huang, Kou Tanaka +3
This paper proposes a voice conversion (VC) method based on a sequence-to-sequence (S2S) learning framework, which enables simultaneous conversion of the voice characteristics, pit…
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…
ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech
Xin Wang, Junichi Yamagishi, Massimiliano Todisco +37
Automatic speaker verification (ASV) is one of the most natural and convenient means of biometric person recognition. Unfortunately, just like all other biometric systems, ASV is v…
Training a Neural Speech Waveform Model using Spectral Losses of Short-Time Fourier Transform and Continuous Wavelet Transform
Shinji Takaki, Hirokazu Kameoka, Junichi Yamagishi
Recently, we proposed short-time Fourier transform (STFT)-based loss functions for training a neural speech waveform model. In this paper, we generalize the above framework and pro…
AttS2S-VC: Sequence-to-Sequence Voice Conversion with Attention and Context Preservation Mechanisms
Kou Tanaka, Hirokazu Kameoka, Takuhiro Kaneko +1
This paper describes a method based on a sequence-to-sequence learning (Seq2Seq) with attention and context preservation mechanism for voice conversion (VC) tasks. Seq2Seq has been…
WaveCycleGAN: Synthetic-to-natural speech waveform conversion using cycle-consistent adversarial networks
Kou Tanaka, Takuhiro Kaneko, Nobukatsu Hojo +1
We propose a learning-based filter that allows us to directly modify a synthetic speech waveform into a natural speech waveform. Speech-processing systems using a vocoder framework…
Speech waveform synthesis from MFCC sequences with generative adversarial networks
Lauri Juvela, Bajibabu Bollepalli, Xin Wang +4
This paper proposes a method for generating speech from filterbank mel frequency cepstral coefficients (MFCC), which are widely used in speech applications, such as ASR, but are ge…