8 citations · 33 across the 15 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…
Does the Lombard Effect Improve Emotional Communication in Noise? - Analysis of Emotional Speech Acted in Noise -
Yi Zhao, Atsushi Ando, Shinji Takaki +2
Speakers usually adjust their way of talking in noisy environments involuntarily for effective communication. This adaptation is known as the Lombard effect. Although speech accomp…
Training a Neural Speech Waveform Model using Spectral Losses of Short-Time Fourier Transform and Continuous Wavelet Transform
Shinji Takaki, Hirokazu Kameoka, Junichi Yamagishi
Recently, we proposed short-time Fourier transform (STFT)-based loss functions for training a neural speech waveform model. In this paper, we generalize the above framework and pro…
Neural source-filter waveform models for statistical parametric speech synthesis
Xin Wang, Shinji Takaki, Junichi Yamagishi
Neural waveform models such as WaveNet have demonstrated better performance than conventional vocoders for statistical parametric speech synthesis. As an autoregressive (AR) model,…
STFT spectral loss for training a neural speech waveform model
Shinji Takaki, Toru Nakashika, Xin Wang +1
This paper proposes a new loss using short-time Fourier transform (STFT) spectra for the aim of training a high-performance neural speech waveform model that predicts raw continuou…
Investigation of enhanced Tacotron text-to-speech synthesis systems with self-attention for pitch accent language
Yusuke Yasuda, Xin Wang, Shinji Takaki +1
End-to-end speech synthesis is a promising approach that directly converts raw text to speech. Although it was shown that Tacotron2 outperforms classical pipeline systems with rega…
Neural source-filter-based waveform model for statistical parametric speech synthesis
Xin Wang, Shinji Takaki, Junichi Yamagishi
Neural waveform models such as the WaveNet are used in many recent text-to-speech systems, but the original WaveNet is quite slow in waveform generation because of its autoregressi…
Wasserstein GAN and Waveform Loss-based Acoustic Model Training for Multi-speaker Text-to-Speech Synthesis Systems Using a WaveNet Vocoder
Yi Zhao, Shinji Takaki, Hieu-Thi Luong +3
Recent neural networks such as WaveNet and sampleRNN that learn directly from speech waveform samples have achieved very high-quality synthetic speech in terms of both naturalness…
A comparison of recent waveform generation and acoustic modeling methods for neural-network-based speech synthesis
Xin Wang, Jaime Lorenzo-Trueba, Shinji Takaki +2
Recent advances in speech synthesis suggest that limitations such as the lossy nature of the amplitude spectrum with minimum phase approximation and the over-smoothing effect in ac…
Complex-Valued Restricted Boltzmann Machine for Direct Speech Parameterization from Complex Spectra
Toru Nakashika, Shinji Takaki, Junichi Yamagishi
This paper describes a novel energy-based probabilistic distribution that represents complex-valued data and explains how to apply it to direct feature extraction from complex-valu…
Deep Denoising Auto-encoder for Statistical Speech Synthesis
Zhenzhou Wu, Shinji Takaki, Junichi Yamagishi
This paper proposes a deep denoising auto-encoder technique to extract better acoustic features for speech synthesis. The technique allows us to automatically extract low-dimension…