77 citations · 237 across the 65 of their papers we have counts for
13 papers · 1 filter
HASA-net: A non-intrusive hearing-aid speech assessment network
Hsin-Tien Chiang, Yi-Chiao Wu, Cheng Yu +4
Without the need of a clean reference, non-intrusive speech assessment methods have caught great attention for objective evaluations. Recently, deep neural network (DNN) models hav…
Deep Learning-based Non-Intrusive Multi-Objective Speech Assessment Model with Cross-Domain Features
Ryandhimas E. Zezario, Szu-Wei Fu, Fei Chen +3
In this study, we propose a cross-domain multi-objective speech assessment model called MOSA-Net, which can estimate multiple speech assessment metrics simultaneously. Experimental…
Speech Enhancement-assisted Voice Conversion in Noisy Environments
Yun-Ju Chan, Chiang-Jen Peng, Syu-Siang Wang +3
Numerous voice conversion (VC) techniques have been proposed for the conversion of voices among different speakers. Although good quality of the converted speech can be observed wh…
Time Alignment using Lip Images for Frame-based Electrolaryngeal Voice Conversion
Yi-Syuan Liou, Wen-Chin Huang, Ming-Chi Yen +5
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 de…
SurpriseNet: Melody Harmonization Conditioning on User-controlled Surprise Contours
Yi-Wei Chen, Hung-Shin Lee, Yen-Hsing Chen +1
The surprisingness of a song is an essential and seemingly subjective factor in determining whether the listener likes it. With the help of information theory, it can be described…
AlloST: Low-resource Speech Translation without Source Transcription
Yao-Fei Cheng, Hung-Shin Lee, Hsin-Min Wang
The end-to-end architecture has made promising progress in speech translation (ST). However, the ST task is still challenging under low-resource conditions. Most ST models have sho…
SVSNet: An End-to-end Speaker Voice Similarity Assessment Model
Cheng-Hung Hu, Yu-Huai Peng, Junichi Yamagishi +2
Neural evaluation metrics derived for numerous speech generation tasks have recently attracted great attention. In this paper, we propose SVSNet, the first end-to-end neural networ…
Relational Data Selection for Data Augmentation of Speaker-dependent Multi-band MelGAN Vocoder
Yi-Chiao Wu, Cheng-Hung Hu, Hung-Shin Lee +5
Nowadays, neural vocoders can generate very high-fidelity speech when a bunch of training data is available. Although a speaker-dependent (SD) vocoder usually outperforms a speaker…
A Preliminary Study of a Two-Stage Paradigm for Preserving Speaker Identity in Dysarthric Voice Conversion
Wen-Chin Huang, Kazuhiro Kobayashi, Yu-Huai Peng +4
We propose a new paradigm for maintaining speaker identity in dysarthric voice conversion (DVC). The poor quality of dysarthric speech can be greatly improved by statistical VC, bu…
Sequence to General Tree: Knowledge-Guided Geometry Word Problem Solving
Shih-hung Tsai, Chao-Chun Liang, Hsin-Min Wang +1
With the recent advancements in deep learning, neural solvers have gained promising results in solving math word problems. However, these SOTA solvers only generate binary expressi…
Dual-Path Filter Network: Speaker-Aware Modeling for Speech Separation
Fan-Lin Wang, Yu-Huai Peng, Hung-Shin Lee +1
Speech separation has been extensively studied to deal with the cocktail party problem in recent years. All related approaches can be divided into two categories: time-frequency do…
The AS-NU System for the M2VoC Challenge
Cheng-Hung Hu, Yi-Chiao Wu, Wen-Chin Huang +6
This paper describes the AS-NU systems for two tracks in MultiSpeaker Multi-Style Voice Cloning Challenge (M2VoC). The first track focuses on using a small number of 100 target utt…
Speech Recognition by Simply Fine-tuning BERT
Wen-Chin Huang, Chia-Hua Wu, Shang-Bao Luo +3
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 ri…