Search Results for author:
Found 26 papers, 15 papers with code
AudioDec: An Open-source Streaming High-fidelity Neural Audio Codec
A good audio codec for live applications such as telecommunication is characterized by three key properties: (1) compression, i. e.\ the bitrate that is required to transmit the signal should be as low as possible; (2) latency, i. e.\ encoding and decoding the signal needs to be fast enough to enable communication without or with only minimal noticeable delay; and (3) reconstruction quality of the signal.
Source-Filter HiFi-GAN: Fast and Pitch Controllable High-Fidelity Neural Vocoder
Our previous work, the unified source-filter GAN (uSFGAN) vocoder, introduced a novel architecture based on the source-filter theory into the parallel waveform generative adversarial network to achieve high voice quality and pitch controllability.
A Cyclical Approach to Synthetic and Natural Speech Mismatch Refinement of Neural Post-filter for Low-cost Text-to-speech System
Neural-based text-to-speech (TTS) systems achieve very high-fidelity speech generation because of the rapid neural network developments.
Unified Source-Filter GAN with Harmonic-plus-Noise Source Excitation Generation
To improve the source excitation modeling and generated sound quality, a new source excitation generation network separately generating periodic and aperiodic components is proposed.
HASA-net: A non-intrusive hearing-aid speech assessment network
Without the need of a clean reference, non-intrusive speech assessment methods have caught great attention for objective evaluations.
Relational Data Selection for Data Augmentation of Speaker-dependent Multi-band MelGAN Vocoder
Nowadays, neural vocoders can generate very high-fidelity speech when a bunch of training data is available.
Unified Source-Filter GAN: Unified Source-filter Network Based On Factorization of Quasi-Periodic Parallel WaveGAN
We propose a unified approach to data-driven source-filter modeling using a single neural network for developing a neural vocoder capable of generating high-quality synthetic speech waveforms while retaining flexibility of the source-filter model to control their voice characteristics.
The AS-NU System for the M2VoC Challenge
The first track focuses on using a small number of 100 target utterances for voice cloning, while the second track focuses on using only 5 target utterances for voice cloning.
crank: An Open-Source Software for Nonparallel Voice Conversion Based on Vector-Quantized Variational Autoencoder
In this paper, we present an open-source software for developing a nonparallel voice conversion (VC) system named crank.
Any-to-One Sequence-to-Sequence Voice Conversion using Self-Supervised Discrete Speech Representations
Given a training dataset of the target speaker, we extract VQW2V and acoustic features to estimate a seq2seq mapping function from the former to the latter.
The NU Voice Conversion System for the Voice Conversion Challenge 2020: On the Effectiveness of Sequence-to-sequence Models and Autoregressive Neural Vocoders
In this paper, we present the voice conversion (VC) systems developed at Nagoya University (NU) for the Voice Conversion Challenge 2020 (VCC2020).
Baseline System of Voice Conversion Challenge 2020 with Cyclic Variational Autoencoder and Parallel WaveGAN
In this paper, we present a description of the baseline system of Voice Conversion Challenge (VCC) 2020 with a cyclic variational autoencoder (CycleVAE) and Parallel WaveGAN (PWG), i. e., CycleVAEPWG.
Pretraining Techniques for Sequence-to-Sequence Voice Conversion
Sequence-to-sequence (seq2seq) voice conversion (VC) models are attractive owing to their ability to convert prosody.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Quasi-Periodic Parallel WaveGAN: A Non-autoregressive Raw Waveform Generative Model with Pitch-dependent Dilated Convolution Neural Network
To improve the pitch controllability and speech modeling capability, we apply a QP structure with PDCNNs to PWG, which introduces pitch information to the network by dynamically changing the network architecture corresponding to the auxiliary $F_{0}$ feature.
Quasi-Periodic WaveNet: An Autoregressive Raw Waveform Generative Model with Pitch-dependent Dilated Convolution Neural Network
In this paper, a pitch-adaptive waveform generative model named Quasi-Periodic WaveNet (QPNet) is proposed to improve the limited pitch controllability of vanilla WaveNet (WN) using pitch-dependent dilated convolution neural networks (PDCNNs).
Quasi-Periodic Parallel WaveGAN Vocoder: A Non-autoregressive Pitch-dependent Dilated Convolution Model for Parametric Speech Generation
In this paper, we propose a parallel WaveGAN (PWG)-like neural vocoder with a quasi-periodic (QP) architecture to improve the pitch controllability of PWG.
Audio and Speech Processing Sound
Voice Transformer Network: Sequence-to-Sequence Voice Conversion Using Transformer with Text-to-Speech Pretraining
We introduce a novel sequence-to-sequence (seq2seq) voice conversion (VC) model based on the Transformer architecture with text-to-speech (TTS) pretraining.
ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech
Spoofing attacks within a logical access (LA) scenario are generated with the latest speech synthesis and voice conversion technologies, including state-of-the-art neural acoustic and waveform model techniques.
Non-Parallel Voice Conversion with Cyclic Variational Autoencoder
In this work, to overcome this problem, we propose to use CycleVAE-based spectral model that indirectly optimizes the conversion flow by recycling the converted features back into the system to obtain corresponding cyclic reconstructed spectra that can be directly optimized.
Statistical Voice Conversion with Quasi-Periodic WaveNet Vocoder
However, because of the fixed dilated convolution and generic network architecture, the WN vocoder lacks robustness against unseen input features and often requires a huge network size to achieve acceptable speech quality.
Audio and Speech Processing Sound
Quasi-Periodic WaveNet Vocoder: A Pitch Dependent Dilated Convolution Model for Parametric Speech Generation
In this paper, we propose a quasi-periodic neural network (QPNet) vocoder with a novel network architecture named pitch-dependent dilated convolution (PDCNN) to improve the pitch controllability of WaveNet (WN) vocoder.
Investigation of F0 conditioning and Fully Convolutional Networks in Variational Autoencoder based Voice Conversion
Such hypothesis implies that during the conversion phase, the latent codes and the converted features in VAE based VC are in fact source F0 dependent.
Refined WaveNet Vocoder for Variational Autoencoder Based Voice Conversion
Conventional WaveNet vocoders are trained with natural acoustic features but conditioned on the converted features in the conversion stage for VC, and such a mismatch often causes significant quality and similarity degradation.
Voice Conversion from Unaligned Corpora using Variational Autoencoding Wasserstein Generative Adversarial Networks
Building a voice conversion (VC) system from non-parallel speech corpora is challenging but highly valuable in real application scenarios.
Voice Conversion from Non-parallel Corpora Using Variational Auto-encoder
We propose a flexible framework for spectral conversion (SC) that facilitates training with unaligned corpora.
Dictionary Update for NMF-based Voice Conversion Using an Encoder-Decoder Network
In this paper, we propose a dictionary update method for Nonnegative Matrix Factorization (NMF) with high dimensional data in a spectral conversion (SC) task.
