2 citations · 4 across the 9 of their papers we have counts for
9 papers
Sarashina2.2-TTS: Tackling Kanji Polyphony in Japanese Speech Generation via Data Scaling and Targeted Data Synthesis
Lianbo Liu, Shiao Zhu, Kai Washizaki +10
While large language model (LLM)-based text-to-speech (TTS) systems have achieved high-quality speech synthesis, most existing systems focus on English and Chinese. Japanese, howev…
Comparative Analysis of Fast and High-Fidelity Neural Vocoders for Low-Latency Streaming Synthesis in Resource-Constrained Environments
Reo Yoneyama, Masaya Kawamura, Ryo Terashima +2
In real-time speech synthesis, neural vocoders often require low-latency synthesis through causal processing and streaming. However, streaming introduces inefficiencies absent in b…
Wavehax: Aliasing-Free Neural Waveform Synthesis Based on 2D Convolution and Harmonic Prior for Reliable Complex Spectrogram Estimation
Reo Yoneyama, Atsushi Miyashita, Ryuichi Yamamoto +1
Neural vocoders often struggle with aliasing in latent feature spaces, caused by time-domain nonlinear operations and resampling layers. Aliasing folds high-frequency components in…
A Comparative Study of Voice Conversion Models with Large-Scale Speech and Singing Data: The T13 Systems for the Singing Voice Conversion Challenge 2023
Ryuichi Yamamoto, Reo Yoneyama, Lester Phillip Violeta +2
This paper presents our systems (denoted as T13) for the singing voice conversion challenge (SVCC) 2023. For both in-domain and cross-domain English singing voice conversion (SVC)…
NNSVS: A Neural Network-Based Singing Voice Synthesis Toolkit
Ryuichi Yamamoto, Reo Yoneyama, Tomoki Toda
This paper describes the design of NNSVS, an open-source software for neural network-based singing voice synthesis research. NNSVS is inspired by Sinsy, an open-source pioneer in s…
Nonparallel High-Quality Audio Super Resolution with Domain Adaptation and Resampling CycleGANs
Reo Yoneyama, Ryuichi Yamamoto, Kentaro Tachibana
Neural audio super-resolution models are typically trained on low- and high-resolution audio signal pairs. Although these methods achieve highly accurate super-resolution if the ac…
Source-Filter HiFi-GAN: Fast and Pitch Controllable High-Fidelity Neural Vocoder
Reo Yoneyama, Yi-Chiao Wu, Tomoki Toda
Our previous work, the unified source-filter GAN (uSFGAN) vocoder, introduced a novel architecture based on the source-filter theory into the parallel waveform generative adversari…
Unified Source-Filter GAN with Harmonic-plus-Noise Source Excitation Generation
Reo Yoneyama, Yi-Chiao Wu, Tomoki Toda
This paper introduces a unified source-filter network with a harmonic-plus-noise source excitation generation mechanism. In our previous work, we proposed unified Source-Filter GAN…
Unified Source-Filter GAN: Unified Source-filter Network Based On Factorization of Quasi-Periodic Parallel WaveGAN
Reo Yoneyama, Yi-Chiao Wu, Tomoki Toda
We propose a unified approach to data-driven source-filter modeling using a single neural network for developing a neural vocoder capable of generating high-quality synthetic speec…