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Found 14 papers, 4 papers with code
Embedding a Differentiable Mel-cepstral Synthesis Filter to a Neural Speech Synthesis System
This paper integrates a classic mel-cepstral synthesis filter into a modern neural speech synthesis system towards end-to-end controllable speech synthesis.
Neural Sequence-to-Sequence Speech Synthesis Using a Hidden Semi-Markov Model Based Structured Attention Mechanism
This paper proposes a novel Sequence-to-Sequence (Seq2Seq) model integrating the structure of Hidden Semi-Markov Models (HSMMs) into its attention mechanism.
PeriodNet: A non-autoregressive waveform generation model with a structure separating periodic and aperiodic components
We also show that the speech waveforms with a pitch outside of the training data range can be generated with more naturalness.
Transformation of low-quality device-recorded speech to high-quality speech using improved SEGAN model
Nowadays vast amounts of speech data are recorded from low-quality recorder devices such as smartphones, tablets, laptops, and medium-quality microphones.
Sound Audio and Speech Processing
Fast and High-Quality Singing Voice Synthesis System based on Convolutional Neural Networks
Singing voice synthesis systems based on deep neural networks (DNNs) are currently being proposed and are improving the naturalness of synthesized singing voices.
Neural source-filter waveform models for statistical parametric speech synthesis
Other models such as Parallel WaveNet and ClariNet bring together the benefits of AR and IAF-based models and train an IAF model by transferring the knowledge from a pre-trained AR teacher to an IAF student without any sequential transformation.
Training a Neural Speech Waveform Model using Spectral Losses of Short-Time Fourier Transform and Continuous Wavelet Transform
Recently, we proposed short-time Fourier transform (STFT)-based loss functions for training a neural speech waveform model.
STFT spectral loss for training a neural speech waveform model
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 continuous speech waveform samples directly.
Neural source-filter-based waveform model for statistical parametric speech synthesis
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 autoregressive (AR) structure.
Investigation of enhanced Tacotron text-to-speech synthesis systems with self-attention for pitch accent language
Towards end-to-end Japanese speech synthesis, we extend Tacotron to systems with self-attention to capture long-term dependencies related to pitch accents and compare their audio quality with classical pipeline systems under various conditions to show their pros and cons.
Wasserstein GAN and Waveform Loss-based Acoustic Model Training for Multi-speaker Text-to-Speech Synthesis Systems Using a WaveNet Vocoder
In order to reduce the mismatched characteristics between natural and generated acoustic features, we propose frameworks that incorporate either a conditional generative adversarial network (GAN) or its variant, Wasserstein GAN with gradient penalty (WGAN-GP), into multi-speaker speech synthesis that uses the WaveNet vocoder.
A comparison of recent waveform generation and acoustic modeling methods for neural-network-based speech synthesis
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 acoustic modeling can be overcome by using advanced machine learning approaches.
Complex-Valued Restricted Boltzmann Machine for Direct Speech Parameterization from Complex Spectra
In contrast, the proposed feature extractor using the CRBM directly encodes the complex spectra (or another complex-valued representation of the complex spectra) into binary-valued latent features (hidden units).
Deep Denoising Auto-encoder for Statistical Speech Synthesis
This paper proposes a deep denoising auto-encoder technique to extract better acoustic features for speech synthesis.
