788 citations · 938 across the 35 of their papers we have counts for
8 papers · 1 filter
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…
Lightweight and High-Fidelity End-to-End Text-to-Speech with Multi-Band Generation and Inverse Short-Time Fourier Transform
Masaya Kawamura, Yuma Shirahata, Ryuichi Yamamoto +1
We propose a lightweight end-to-end text-to-speech model using multi-band generation and inverse short-time Fourier transform. Our model is based on VITS, a high-quality end-to-end…
Period VITS: Variational Inference with Explicit Pitch Modeling for End-to-end Emotional Speech Synthesis
Yuma Shirahata, Ryuichi Yamamoto, Eunwoo Song +3
Several fully end-to-end text-to-speech (TTS) models have been proposed that have shown better performance compared to cascade models (i.e., training acoustic and vocoder models se…
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…
Language Model-Based Emotion Prediction Methods for Emotional Speech Synthesis Systems
Hyun-Wook Yoon, Ohsung Kwon, Hoyeon Lee +4
This paper proposes an effective emotional text-to-speech (TTS) system with a pre-trained language model (LM)-based emotion prediction method. Unlike conventional systems that requ…
TTS-by-TTS 2: Data-selective augmentation for neural speech synthesis using ranking support vector machine with variational autoencoder
Eunwoo Song, Ryuichi Yamamoto, Ohsung Kwon +6
Recent advances in synthetic speech quality have enabled us to train text-to-speech (TTS) systems by using synthetic corpora. However, merely increasing the amount of synthetic dat…
Cross-Speaker Emotion Transfer for Low-Resource Text-to-Speech Using Non-Parallel Voice Conversion with Pitch-Shift Data Augmentation
Ryo Terashima, Ryuichi Yamamoto, Eunwoo Song +4
Data augmentation via voice conversion (VC) has been successfully applied to low-resource expressive text-to-speech (TTS) when only neutral data for the target speaker are availabl…
DRSpeech: Degradation-Robust Text-to-Speech Synthesis with Frame-Level and Utterance-Level Acoustic Representation Learning
Takaaki Saeki, Kentaro Tachibana, Ryuichi Yamamoto
Most text-to-speech (TTS) methods use high-quality speech corpora recorded in a well-designed environment, incurring a high cost for data collection. To solve this problem, existin…