Search Results for author:
Found 62 papers, 21 papers with code
Investigation of Japanese PnG BERT language model in text-to-speech synthesis for pitch accent language
To tackle the challenge of rendering correct pitch accent in Japanese end-to-end TTS, we adopt PnG~BERT, a self-supervised pretrained model in the character and phoneme domain for TTS.
Text-to-speech synthesis based on latent variable conversion using diffusion probabilistic model and variational autoencoder
We propose a TTS method based on latent variable conversion using a diffusion probabilistic model and the variational autoencoder (VAE).
Analysis of Noisy-target Training for DNN-based speech enhancement
To relax this limitation, Noisy-target Training (NyTT) that utilizes noisy speech as a training target has been proposed.
NNSVS: A Neural Network-Based Singing Voice Synthesis Toolkit
This paper describes the design of NNSVS, an open-source software for neural network-based singing voice synthesis research.
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.
A Comparative Study of Self-supervised Speech Representation Based Voice Conversion
We present a large-scale comparative study of self-supervised speech representation (S3R)-based voice conversion (VC).
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.
LDNet: Unified Listener Dependent Modeling in MOS Prediction for Synthetic Speech
An effective approach to automatically predict the subjective rating for synthetic speech is to train on a listening test dataset with human-annotated scores.
Towards Identity Preserving Normal to Dysarthric Voice Conversion
We present a voice conversion framework that converts normal speech into dysarthric speech while preserving the speaker identity.
S3PRL-VC: Open-source Voice Conversion Framework with Self-supervised Speech Representations
In this work, we provide a series of in-depth analyses by benchmarking on the two tasks in VCC2020, namely intra-/cross-lingual any-to-one (A2O) VC, as well as an any-to-any (A2A) setting.
Generalization Ability of MOS Prediction Networks
Automatic methods to predict listener opinions of synthesized speech remain elusive since listeners, systems being evaluated, characteristics of the speech, and even the instructions given and the rating scale all vary from test to test.
Time Alignment using Lip Images for Frame-based Electrolaryngeal Voice Conversion
Voice conversion (VC) is an effective approach to electrolaryngeal (EL) speech enhancement, a task that aims to improve the quality of the artificial voice from an electrolarynx device.
On Prosody Modeling for ASR+TTS based Voice Conversion
In voice conversion (VC), an approach showing promising results in the latest voice conversion challenge (VCC) 2020 is to first use an automatic speech recognition (ASR) model to transcribe the source speech into the underlying linguistic contents; these are then used as input by a text-to-speech (TTS) system to generate the converted speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Anomalous Sound Detection Using a Binary Classification Model and Class Centroids
Our results showed that multi-task learning using binary classification and metric learning to consider the distance from each class centroid in the feature space is effective, and performance can be significantly improved by using even a small amount of anomalous data during training.
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.
A Preliminary Study of a Two-Stage Paradigm for Preserving Speaker Identity in Dysarthric Voice Conversion
First, a powerful parallel sequence-to-sequence model converts the input dysarthric speech into a normal speech of a reference speaker as an intermediate product, and a nonparallel, frame-wise VC model realized with a variational autoencoder then converts the speaker identity of the reference speech back to that of the patient while assumed to be capable of preserving the enhanced quality.
High-Fidelity and Low-Latency Universal Neural Vocoder based on Multiband WaveRNN with Data-Driven Linear Prediction for Discrete Waveform Modeling
This paper presents a novel high-fidelity and low-latency universal neural vocoder framework based on multiband WaveRNN with data-driven linear prediction for discrete waveform modeling (MWDLP).
Low-Latency Real-Time Non-Parallel Voice Conversion based on Cyclic Variational Autoencoder and Multiband WaveRNN with Data-Driven Linear Prediction
To accommodate LLRT constraint with CPU, we propose a novel CycleVAE framework that utilizes mel-spectrogram as spectral features and is built with a sparse network architecture.
Non-autoregressive sequence-to-sequence voice conversion
This paper proposes a novel voice conversion (VC) method based on non-autoregressive sequence-to-sequence (NAR-S2S) models.
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.
Speech Recognition by Simply Fine-tuning BERT
We propose a simple method for automatic speech recognition (ASR) by fine-tuning BERT, which is a language model (LM) trained on large-scale unlabeled text data and can generate rich contextual representations.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
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.
The Sequence-to-Sequence Baseline for the Voice Conversion Challenge 2020: Cascading ASR and TTS
This paper presents the sequence-to-sequence (seq2seq) baseline system for the voice conversion challenge (VCC) 2020.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
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)
+2
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).
Many-to-Many Voice Transformer Network
The main idea we propose is an extension of the original VTN that can simultaneously learn mappings among multiple speakers.
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.
ESPnet-TTS: Unified, Reproducible, and Integratable Open Source End-to-End Text-to-Speech Toolkit
Furthermore, the unified design enables the integration of ASR functions with TTS, e. g., ASR-based objective evaluation and semi-supervised learning with both ASR and TTS models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
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.
Generalized Multichannel Variational Autoencoder for Underdetermined Source Separation
This paper deals with a multichannel audio source separation problem under underdetermined conditions.
Back-Translation-Style Data Augmentation for End-to-End ASR
In this paper we propose a novel data augmentation method for attention-based end-to-end automatic speech recognition (E2E-ASR), utilizing a large amount of text which is not paired with speech signals.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Frequency domain variants of velvet noise and their application to speech processing and synthesis: with appendices
One of the proposed variants, FVN (Frequency domain Velvet Noise) applies the procedure to generate a velvet noise on the cyclic frequency domain of DFT (Discrete Fourier Transform).
Sound Audio and Speech Processing Signal Processing
A Spoofing Benchmark for the 2018 Voice Conversion Challenge: Leveraging from Spoofing Countermeasures for Speech Artifact Assessment
As a supplement to subjective results for the 2018 Voice Conversion Challenge (VCC'18) data, we configure a standard constant-Q cepstral coefficient CM to quantify the extent of processing artifacts.
Multi-Head Decoder for End-to-End Speech Recognition
This paper presents a new network architecture called multi-head decoder for end-to-end speech recognition as an extension of a multi-head attention model.
The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods
We present the Voice Conversion Challenge 2018, designed as a follow up to the 2016 edition with the aim of providing a common framework for evaluating and comparing different state-of-the-art voice conversion (VC) systems.
Semantic Parsing of Ambiguous Input through Paraphrasing and Verification
We propose a new method for semantic parsing of ambiguous and ungrammatical input, such as search queries.
Towards Multilingual Conversations in the Medical Domain: Development of Multilingual Medical Data and A Network-based ASR System
This paper outlines the recent development on multilingual medical data and multilingual speech recognition system for network-based speech-to-speech translation in the medical domain.
Collection of a Simultaneous Translation Corpus for Comparative Analysis
This makes it possible to compare translation data with simultaneous interpretation data.
