Search Results for author: Wen-Chin Huang

Found 31 papers, 14 papers with code

Evaluating Methods for Ground-Truth-Free Foreign Accent Conversion

1 code implementation5 Sep 2023 Wen-Chin Huang, Tomoki Toda

In this work, we evaluate three recently proposed methods for ground-truth-free FAC, where all of them aim to harness the power of sequence-to-sequence (seq2seq) and non-parallel VC models to properly convert the accent and control the speaker identity.

Voice Conversion

The Singing Voice Conversion Challenge 2023

1 code implementation26 Jun 2023 Wen-Chin Huang, Lester Phillip Violeta, Songxiang Liu, Jiatong Shi, Tomoki Toda

A new database was constructed for two tasks, namely in-domain and cross-domain SVC.

Voice Conversion

A Comparative Study of Self-supervised Speech Representation Based Voice Conversion

no code implementations10 Jul 2022 Wen-Chin Huang, Shu-wen Yang, Tomoki Hayashi, Tomoki Toda

We present a large-scale comparative study of self-supervised speech representation (S3R)-based voice conversion (VC).

Voice Conversion

LDNet: Unified Listener Dependent Modeling in MOS Prediction for Synthetic Speech

1 code implementation18 Oct 2021 Wen-Chin Huang, Erica Cooper, Junichi Yamagishi, Tomoki Toda

An effective approach to automatically predict the subjective rating for synthetic speech is to train on a listening test dataset with human-annotated scores.

Voice Conversion

Towards Identity Preserving Normal to Dysarthric Voice Conversion

no code implementations15 Oct 2021 Wen-Chin Huang, Bence Mark Halpern, Lester Phillip Violeta, Odette Scharenborg, Tomoki Toda

We present a voice conversion framework that converts normal speech into dysarthric speech while preserving the speaker identity.

Data Augmentation Decision Making +3

S3PRL-VC: Open-source Voice Conversion Framework with Self-supervised Speech Representations

1 code implementation12 Oct 2021 Wen-Chin Huang, Shu-wen Yang, Tomoki Hayashi, Hung-Yi Lee, Shinji Watanabe, Tomoki Toda

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.

Benchmarking Voice Conversion

Generalization Ability of MOS Prediction Networks

1 code implementation6 Oct 2021 Erica Cooper, Wen-Chin Huang, Tomoki Toda, Junichi Yamagishi

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

no code implementations8 Sep 2021 Yi-Syuan Liou, Wen-Chin Huang, Ming-Chi Yen, Shu-Wei Tsai, Yu-Huai Peng, Tomoki Toda, Yu Tsao, Hsin-Min Wang

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.

Dynamic Time Warping Speech Enhancement +1

On Prosody Modeling for ASR+TTS based Voice Conversion

no code implementations20 Jul 2021 Wen-Chin Huang, Tomoki Hayashi, Xinjian Li, Shinji Watanabe, Tomoki Toda

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

A Preliminary Study of a Two-Stage Paradigm for Preserving Speaker Identity in Dysarthric Voice Conversion

no code implementations2 Jun 2021 Wen-Chin Huang, Kazuhiro Kobayashi, Yu-Huai Peng, Ching-Feng Liu, Yu Tsao, Hsin-Min Wang, Tomoki Toda

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.

Voice Conversion

Non-autoregressive sequence-to-sequence voice conversion

no code implementations14 Apr 2021 Tomoki Hayashi, Wen-Chin Huang, Kazuhiro Kobayashi, Tomoki Toda

This paper proposes a novel voice conversion (VC) method based on non-autoregressive sequence-to-sequence (NAR-S2S) models.

Voice Conversion

The AS-NU System for the M2VoC Challenge

no code implementations7 Apr 2021 Cheng-Hung Hu, Yi-Chiao Wu, Wen-Chin Huang, Yu-Huai Peng, Yu-Wen Chen, Pin-Jui Ku, Tomoki Toda, Yu Tsao, Hsin-Min Wang

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.

Voice Cloning

EMA2S: An End-to-End Multimodal Articulatory-to-Speech System

no code implementations7 Feb 2021 Yu-Wen Chen, Kuo-Hsuan Hung, Shang-Yi Chuang, Jonathan Sherman, Wen-Chin Huang, Xugang Lu, Yu Tsao

Synthesized speech from articulatory movements can have real-world use for patients with vocal cord disorders, situations requiring silent speech, or in high-noise environments.

Speech Recognition by Simply Fine-tuning BERT

no code implementations30 Jan 2021 Wen-Chin Huang, Chia-Hua Wu, Shang-Bao Luo, Kuan-Yu Chen, Hsin-Min Wang, Tomoki Toda

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

no code implementations23 Oct 2020 Wen-Chin Huang, Yi-Chiao Wu, Tomoki Hayashi, Tomoki Toda

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.

Voice Conversion

Many-to-Many Voice Transformer Network

no code implementations18 May 2020 Hirokazu Kameoka, Wen-Chin Huang, Kou Tanaka, Takuhiro Kaneko, Nobukatsu Hojo, Tomoki Toda

The main idea we propose is an extension of the original VTN that can simultaneously learn mappings among multiple speakers.

Voice Conversion

Unsupervised Representation Disentanglement using Cross Domain Features and Adversarial Learning in Variational Autoencoder based Voice Conversion

1 code implementation22 Jan 2020 Wen-Chin Huang, Hao Luo, Hsin-Te Hwang, Chen-Chou Lo, Yu-Huai Peng, Yu Tsao, Hsin-Min Wang

In this paper, we extend the CDVAE-VC framework by incorporating the concept of adversarial learning, in order to further increase the degree of disentanglement, thereby improving the quality and similarity of converted speech.

Disentanglement Voice Conversion

Voice Transformer Network: Sequence-to-Sequence Voice Conversion Using Transformer with Text-to-Speech Pretraining

1 code implementation14 Dec 2019 Wen-Chin Huang, Tomoki Hayashi, Yi-Chiao Wu, Hirokazu Kameoka, Tomoki Toda

We introduce a novel sequence-to-sequence (seq2seq) voice conversion (VC) model based on the Transformer architecture with text-to-speech (TTS) pretraining.

Voice Conversion

MOSNet: Deep Learning based Objective Assessment for Voice Conversion

6 code implementations17 Apr 2019 Chen-Chou Lo, Szu-Wei Fu, Wen-Chin Huang, Xin Wang, Junichi Yamagishi, Yu Tsao, Hsin-Min Wang

In this paper, we propose deep learning-based assessment models to predict human ratings of converted speech.

Voice Conversion

Refined WaveNet Vocoder for Variational Autoencoder Based Voice Conversion

no code implementations27 Nov 2018 Wen-Chin Huang, Yi-Chiao Wu, Hsin-Te Hwang, Patrick Lumban Tobing, Tomoki Hayashi, Kazuhiro Kobayashi, Tomoki Toda, Yu Tsao, Hsin-Min Wang

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

Voice Conversion Based on Cross-Domain Features Using Variational Auto Encoders

1 code implementation29 Aug 2018 Wen-Chin Huang, Hsin-Te Hwang, Yu-Huai Peng, Yu Tsao, Hsin-Min Wang

An effective approach to non-parallel voice conversion (VC) is to utilize deep neural networks (DNNs), specifically variational auto encoders (VAEs), to model the latent structure of speech in an unsupervised manner.

Voice Conversion

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