4 citations · 9 across the 6 of their papers we have counts for
16 papers
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…
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…
Transformation of low-quality device-recorded speech to high-quality speech using improved SEGAN model
Seyyed Saeed Sarfjoo, Xin Wang, Gustav Eje Henter +3
Nowadays vast amounts of speech data are recorded from low-quality recorder devices such as smartphones, tablets, laptops, and medium-quality microphones. The objective of this res…
Modeling of Rakugo Speech and Its Limitations: Toward Speech Synthesis That Entertains Audiences
Shuhei Kato, Yusuke Yasuda, Xin Wang +3
We have been investigating rakugo speech synthesis as a challenging example of speech synthesis that entertains audiences. Rakugo is a traditional Japanese form of verbal entertain…
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…