41 citations · 100 across the 25 of their papers we have counts for
21 papers · 1 filter
Spatial Voice Conversion: Voice Conversion Preserving Spatial Information and Non-target Signals
Kentaro Seki, Shinnosuke Takamichi, Norihiro Takamune +3
This paper proposes a new task called spatial voice conversion, which aims to convert a target voice while preserving spatial information and non-target signals. Traditional voice…
Noise-Robust Voice Conversion by Conditional Denoising Training Using Latent Variables of Recording Quality and Environment
Takuto Igarashi, Yuki Saito, Kentaro Seki +4
We propose noise-robust voice conversion (VC) which takes into account the recording quality and environment of noisy source speech. Conventional denoising training improves the no…
SRC4VC: Smartphone-Recorded Corpus for Voice Conversion Benchmark
Yuki Saito, Takuto Igarashi, Kentaro Seki +4
We present SRC4VC, a new corpus containing 11 hours of speech recorded on smartphones by 100 Japanese speakers. Although high-quality multi-speaker corpora can advance voice conver…
Building speech corpus with diverse voice characteristics for its prompt-based representation
Aya Watanabe, Shinnosuke Takamichi, Yuki Saito +3
In text-to-speech synthesis, the ability to control voice characteristics is vital for various applications. By leveraging thriving text prompt-based generation techniques, it shou…
JVNV: A Corpus of Japanese Emotional Speech with Verbal Content and Nonverbal Expressions
Detai Xin, Junfeng Jiang, Shinnosuke Takamichi +3
We present the JVNV, a Japanese emotional speech corpus with verbal content and nonverbal vocalizations whose scripts are generated by a large-scale language model. Existing emotio…
Coco-Nut: Corpus of Japanese Utterance and Voice Characteristics Description for Prompt-based Control
Aya Watanabe, Shinnosuke Takamichi, Yuki Saito +3
In text-to-speech, controlling voice characteristics is important in achieving various-purpose speech synthesis. Considering the success of text-conditioned generation, such as tex…
ChatGPT-EDSS: Empathetic Dialogue Speech Synthesis Trained from ChatGPT-derived Context Word Embeddings
Yuki Saito, Shinnosuke Takamichi, Eiji Iimori +2
We propose ChatGPT-EDSS, an empathetic dialogue speech synthesis (EDSS) method using ChatGPT for extracting dialogue context. ChatGPT is a chatbot that can deeply understand the co…
CALLS: Japanese Empathetic Dialogue Speech Corpus of Complaint Handling and Attentive Listening in Customer Center
Yuki Saito, Eiji Iimori, Shinnosuke Takamichi +2
We present CALLS, a Japanese speech corpus that considers phone calls in a customer center as a new domain of empathetic spoken dialogue. The existing STUDIES corpus covers only em…
Mid-attribute speaker generation using optimal-transport-based interpolation of Gaussian mixture models
Aya Watanabe, Shinnosuke Takamichi, Yuki Saito +2
In this paper, we propose a method for intermediating multiple speakers' attributes and diversifying their voice characteristics in ``speaker generation,'' an emerging task that ai…
Multi-Task Adversarial Training Algorithm for Multi-Speaker Neural Text-to-Speech
Yusuke Nakai, Yuki Saito, Kenta Udagawa +1
We propose a novel training algorithm for a multi-speaker neural text-to-speech (TTS) model based on multi-task adversarial training. A conventional generative adversarial network…
Human-in-the-loop Speaker Adaptation for DNN-based Multi-speaker TTS
Kenta Udagawa, Yuki Saito, Hiroshi Saruwatari
This paper proposes a human-in-the-loop speaker-adaptation method for multi-speaker text-to-speech. With a conventional speaker-adaptation method, a target speaker's embedding vect…
Acoustic Modeling for End-to-End Empathetic Dialogue Speech Synthesis Using Linguistic and Prosodic Contexts of Dialogue History
Yuto Nishimura, Yuki Saito, Shinnosuke Takamichi +2
We propose an end-to-end empathetic dialogue speech synthesis (DSS) model that considers both the linguistic and prosodic contexts of dialogue history. Empathy is the active attemp…
STUDIES: Corpus of Japanese Empathetic Dialogue Speech Towards Friendly Voice Agent
Yuki Saito, Yuto Nishimura, Shinnosuke Takamichi +2
We present STUDIES, a new speech corpus for developing a voice agent that can speak in a friendly manner. Humans naturally control their speech prosody to empathize with each other…
Lifter Training and Sub-band Modeling for Computationally Efficient and High-Quality Voice Conversion Using Spectral Differentials
Takaaki Saeki, Yuki Saito, Shinnosuke Takamichi +1
In this paper, we propose computationally efficient and high-quality methods for statistical voice conversion (VC) with direct waveform modification based on spectral differentials…
HumanGAN: generative adversarial network with human-based discriminator and its evaluation in speech perception modeling
Kazuki Fujii, Yuki Saito, Shinnosuke Takamichi +2
We propose the HumanGAN, a generative adversarial network (GAN) incorporating human perception as a discriminator. A basic GAN trains a generator to represent a real-data distribut…
JVS corpus: free Japanese multi-speaker voice corpus
Shinnosuke Takamichi, Kentaro Mitsui, Yuki Saito +3
Thanks to improvements in machine learning techniques, including deep learning, speech synthesis is becoming a machine learning task. To accelerate speech synthesis research, we ar…
V2S attack: building DNN-based voice conversion from automatic speaker verification
Taiki Nakamura, Yuki Saito, Shinnosuke Takamichi +2
This paper presents a new voice impersonation attack using voice conversion (VC). Enrolling personal voices for automatic speaker verification (ASV) offers natural and flexible bio…
Generative Moment Matching Network-based Random Modulation Post-filter for DNN-based Singing Voice Synthesis and Neural Double-tracking
Hiroki Tamaru, Yuki Saito, Shinnosuke Takamichi +2
This paper proposes a generative moment matching network (GMMN)-based post-filter that provides inter-utterance pitch variation for deep neural network (DNN)-based singing voice sy…
Phase reconstruction from amplitude spectrograms based on von-Mises-distribution deep neural network
Shinnosuke Takamichi, Yuki Saito, Norihiro Takamune +2
This paper presents a deep neural network (DNN)-based phase reconstruction from amplitude spectrograms. In audio signal and speech processing, the amplitude spectrogram is often us…
Statistical Parametric Speech Synthesis Incorporating Generative Adversarial Networks
Yuki Saito, Shinnosuke Takamichi, Hiroshi Saruwatari
A method for statistical parametric speech synthesis incorporating generative adversarial networks (GANs) is proposed. Although powerful deep neural networks (DNNs) techniques can…
Voice Conversion Using Sequence-to-Sequence Learning of Context Posterior Probabilities
Hiroyuki Miyoshi, Yuki Saito, Shinnosuke Takamichi +1
Voice conversion (VC) using sequence-to-sequence learning of context posterior probabilities is proposed. Conventional VC using shared context posterior probabilities predicts targ…