35 citations · 74 across the 10 of their papers we have counts for
10 papers
PeriodGrad: Towards Pitch-Controllable Neural Vocoder Based on a Diffusion Probabilistic Model
Yukiya Hono, Kei Hashimoto, Yoshihiko Nankaku +1
This paper presents a neural vocoder based on a denoising diffusion probabilistic model (DDPM) incorporating explicit periodic signals as auxiliary conditioning signals. Recently,…
Singing voice synthesis based on frame-level sequence-to-sequence models considering vocal timing deviation
Miku Nishihara, Yukiya Hono, Kei Hashimoto +2
This paper proposes singing voice synthesis (SVS) based on frame-level sequence-to-sequence models considering vocal timing deviation. In SVS, it is essential to synchronize the ti…
Singing Voice Synthesis Based on a Musical Note Position-Aware Attention Mechanism
Yukiya Hono, Kei Hashimoto, Yoshihiko Nankaku +1
This paper proposes a novel sequence-to-sequence (seq2seq) model with a musical note position-aware attention mechanism for singing voice synthesis (SVS). A seq2seq modeling approa…
Embedding a Differentiable Mel-cepstral Synthesis Filter to a Neural Speech Synthesis System
Takenori Yoshimura, Shinji Takaki, Kazuhiro Nakamura +5
This paper integrates a classic mel-cepstral synthesis filter into a modern neural speech synthesis system towards end-to-end controllable speech synthesis. Since the mel-cepstral…
Neural Sequence-to-Sequence Speech Synthesis Using a Hidden Semi-Markov Model Based Structured Attention Mechanism
Yoshihiko Nankaku, Kenta Sumiya, Takenori Yoshimura +4
This paper proposes a novel Sequence-to-Sequence (Seq2Seq) model integrating the structure of Hidden Semi-Markov Models (HSMMs) into its attention mechanism. In speech synthesis, i…
Sinsy: A Deep Neural Network-Based Singing Voice Synthesis System
Yukiya Hono, Kei Hashimoto, Keiichiro Oura +2
This paper presents Sinsy, a deep neural network (DNN)-based singing voice synthesis (SVS) system. In recent years, DNNs have been utilized in statistical parametric SVS systems, a…
PeriodNet: A non-autoregressive waveform generation model with a structure separating periodic and aperiodic components
Yukiya Hono, Shinji Takaki, Kei Hashimoto +3
We propose PeriodNet, a non-autoregressive (non-AR) waveform generation model with a new model structure for modeling periodic and aperiodic components in speech waveforms. The non…
Hierarchical Multi-Grained Generative Model for Expressive Speech Synthesis
Yukiya Hono, Kazuna Tsuboi, Kei Sawada +4
This paper proposes a hierarchical generative model with a multi-grained latent variable to synthesize expressive speech. In recent years, fine-grained latent variables are introdu…
Fast and High-Quality Singing Voice Synthesis System based on Convolutional Neural Networks
Kazuhiro Nakamura, Shinji Takaki, Kei Hashimoto +3
The present paper describes singing voice synthesis based on convolutional neural networks (CNNs). Singing voice synthesis systems based on deep neural networks (DNNs) are currentl…
Singing voice synthesis based on convolutional neural networks
Kazuhiro Nakamura, Kei Hashimoto, Keiichiro Oura +2
The present paper describes a singing voice synthesis based on convolutional neural networks (CNNs). Singing voice synthesis systems based on deep neural networks (DNNs) are curren…