48 citations · 218 across the 30 of their papers we have counts for
30 papers
MeanVoiceFlow: One-step Nonparallel Voice Conversion with Mean Flows
Takuhiro Kaneko, Hirokazu Kameoka, Kou Tanaka +1
In voice conversion (VC) applications, diffusion and flow-matching models have exhibited exceptional speech quality and speaker similarity performances. However, they are limited b…
LatentVoiceGrad: Nonparallel Voice Conversion with Latent Diffusion/Flow-Matching Models
Hirokazu Kameoka, Takuhiro Kaneko, Kou Tanaka +1
Previously, we introduced VoiceGrad, a nonparallel voice conversion (VC) technique enabling mel-spectrogram conversion from source to target speakers using a score-based diffusion…
Vocoder-Projected Feature Discriminator
Takuhiro Kaneko, Hirokazu Kameoka, Kou Tanaka +1
In text-to-speech (TTS) and voice conversion (VC), acoustic features, such as mel spectrograms, are typically used as synthesis or conversion targets owing to their compactness and…
FasterVoiceGrad: Faster One-step Diffusion-Based Voice Conversion with Adversarial Diffusion Conversion Distillation
Takuhiro Kaneko, Hirokazu Kameoka, Kou Tanaka +1
A diffusion-based voice conversion (VC) model (e.g., VoiceGrad) can achieve high speech quality and speaker similarity; however, its conversion process is slow owing to iterative s…
JIS: A Speech Corpus of Japanese Idol Speakers with Various Speaking Styles
Yuto Kondo, Hirokazu Kameoka, Kou Tanaka +1
We construct Japanese Idol Speech Corpus (JIS) to advance research in speech generation AI, including text-to-speech synthesis (TTS) and voice conversion (VC). JIS will facilitate…
Learning to assess subjective impressions from speech
Yuto Kondo, Hirokazu Kameoka, Kou Tanaka +2
We tackle a new task of training neural network models that can assess subjective impressions conveyed through speech and assign scores accordingly, inspired by the work on automat…
Selecting N-lowest scores for training MOS prediction models
Yuto Kondo, Hirokazu Kameoka, Kou Tanaka +1
The automatic speech quality assessment (SQA) has been extensively studied to predict the speech quality without time-consuming questionnaires. Recently, neural-based SQA models ha…
Rethinking Mean Opinion Scores in Speech Quality Assessment: Aggregation through Quantized Distribution Fitting
Yuto Kondo, Hirokazu Kameoka, Kou Tanaka +1
Speech quality assessment (SQA) aims to evaluate the quality of speech samples without relying on time-consuming listener questionnaires. Recent efforts have focused on training ne…
FastVoiceGrad: One-step Diffusion-Based Voice Conversion with Adversarial Conditional Diffusion Distillation
Takuhiro Kaneko, Hirokazu Kameoka, Kou Tanaka +1
Diffusion-based voice conversion (VC) techniques such as VoiceGrad have attracted interest because of their high VC performance in terms of speech quality and speaker similarity. H…
Training Generative Adversarial Network-Based Vocoder with Limited Data Using Augmentation-Conditional Discriminator
Takuhiro Kaneko, Hirokazu Kameoka, Kou Tanaka
A generative adversarial network (GAN)-based vocoder trained with an adversarial discriminator is commonly used for speech synthesis because of its fast, lightweight, and high-qual…
iSTFTNet2: Faster and More Lightweight iSTFT-Based Neural Vocoder Using 1D-2D CNN
Takuhiro Kaneko, Hirokazu Kameoka, Kou Tanaka +1
The inverse short-time Fourier transform network (iSTFTNet) has garnered attention owing to its fast, lightweight, and high-fidelity speech synthesis. It obtains these characterist…
Wave-U-Net Discriminator: Fast and Lightweight Discriminator for Generative Adversarial Network-Based Speech Synthesis
Takuhiro Kaneko, Hirokazu Kameoka, Kou Tanaka +1
In speech synthesis, a generative adversarial network (GAN), training a generator (speech synthesizer) and a discriminator in a min-max game, is widely used to improve speech quali…
iSTFTNet: Fast and Lightweight Mel-Spectrogram Vocoder Incorporating Inverse Short-Time Fourier Transform
Takuhiro Kaneko, Kou Tanaka, Hirokazu Kameoka +1
In recent text-to-speech synthesis and voice conversion systems, a mel-spectrogram is commonly applied as an intermediate representation, and the necessity for a mel-spectrogram vo…
FastS2S-VC: Streaming Non-Autoregressive Sequence-to-Sequence Voice Conversion
Hirokazu Kameoka, Kou Tanaka, Takuhiro Kaneko
This paper proposes a non-autoregressive extension of our previously proposed sequence-to-sequence (S2S) model-based voice conversion (VC) methods. S2S model-based VC methods have…
MaskCycleGAN-VC: Learning Non-parallel Voice Conversion with Filling in Frames
Takuhiro Kaneko, Hirokazu Kameoka, Kou Tanaka +1
Non-parallel voice conversion (VC) is a technique for training voice converters without a parallel corpus. Cycle-consistent adversarial network-based VCs (CycleGAN-VC and CycleGAN-…
CycleGAN-VC3: Examining and Improving CycleGAN-VCs for Mel-spectrogram Conversion
Takuhiro Kaneko, Hirokazu Kameoka, Kou Tanaka +1
Non-parallel voice conversion (VC) is a technique for learning mappings between source and target speeches without using a parallel corpus. Recently, cycle-consistent adversarial n…
VoiceGrad: Non-Parallel Any-to-Many Voice Conversion with Annealed Langevin Dynamics
Hirokazu Kameoka, Takuhiro Kaneko, Kou Tanaka +2
In this paper, we propose a non-parallel any-to-many voice conversion (VC) method termed VoiceGrad. Inspired by WaveGrad, a recently introduced novel waveform generation method, Vo…
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…
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…
StarGAN-VC2: Rethinking Conditional Methods for StarGAN-Based Voice Conversion
Takuhiro Kaneko, Hirokazu Kameoka, Kou Tanaka +1
Non-parallel multi-domain voice conversion (VC) is a technique for learning mappings among multiple domains without relying on parallel data. This is important but challenging owin…
CycleGAN-VC2: Improved CycleGAN-based Non-parallel Voice Conversion
Takuhiro Kaneko, Hirokazu Kameoka, Kou Tanaka +1
Non-parallel voice conversion (VC) is a technique for learning the mapping from source to target speech without relying on parallel data. This is an important task, but it has been…
Crossmodal Voice Conversion
Hirokazu Kameoka, Kou Tanaka, Aaron Valero Puche +2
Humans are able to imagine a person's voice from the person's appearance and imagine the person's appearance from his/her voice. In this paper, we make the first attempt to develop…
WaveCycleGAN2: Time-domain Neural Post-filter for Speech Waveform Generation
Kou Tanaka, Hirokazu Kameoka, Takuhiro Kaneko +1
WaveCycleGAN has recently been proposed to bridge the gap between natural and synthesized speech waveforms in statistical parametric speech synthesis and provides fast inference wi…