262 citations · 1.4k across the 109 of their papers we have counts for
16 papers · 1 filter
MoEVC: A Mixture-of-experts Voice Conversion System with Sparse Gating Mechanism for Accelerating Online Computation
Yu-Tao Chang, Yuan-Hong Yang, Yu-Huai Peng +4
With the recent advancements of deep learning technologies, the performance of voice conversion (VC) in terms of quality and similarity has been significantly improved. However, he…
MITAS: A Compressed Time-Domain Audio Separation Network with Parameter Sharing
Chao-I Tuan, Yuan-Kuei Wu, Hung-yi Lee +1
Deep learning methods have brought substantial advancements in speech separation (SS). Nevertheless, it remains challenging to deploy deep-learning-based models on edge devices. Th…
Cross-scale Attention Model for Acoustic Event Classification
Xugang Lu, Peng Shen, Sheng Li +2
A major advantage of a deep convolutional neural network (CNN) is that the focused receptive field size is increased by stacking multiple convolutional layers. Accordingly, the mod…
Time-Domain Multi-modal Bone/air Conducted Speech Enhancement
Cheng Yu, Kuo-Hsuan Hung, Syu-Siang Wang +3
Previous studies have proven that integrating video signals, as a complementary modality, can facilitate improved performance for speech enhancement (SE). However, video clips usua…
Distributed Microphone Speech Enhancement based on Deep Learning
Syu-Siang Wang, Yu-You Liang, Jeih-weih Hung +3
Speech-related applications deliver inferior performance in complex noise environments. Therefore, this study primarily addresses this problem by introducing speech-enhancement (SE…
ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech
Xin Wang, Junichi Yamagishi, Massimiliano Todisco +37
Automatic speaker verification (ASV) is one of the most natural and convenient means of biometric person recognition. Unfortunately, just like all other biometric systems, ASV is v…
Improving the Intelligibility of Electric and Acoustic Stimulation Speech Using Fully Convolutional Networks Based Speech Enhancement
Natalie Yu-Hsien Wang, Hsiao-Lan Sharon Wang, Tao-Wei Wang +4
The combined electric and acoustic stimulation (EAS) has demonstrated better speech recognition than conventional cochlear implant (CI) and yielded satisfactory performance under q…
A Study of Joint Effect on Denoising Techniques and Visual Cues to Improve Speech Intelligibility in Cochlear Implant Simulation
Rung-Yu Tseng, Tao-Wei Wang, Szu-Wei Fu +2
Speech perception is key to verbal communication. For people with hearing loss, the capability to recognize speech is restricted, particularly in a noisy environment or the situati…
Multichannel Speech Enhancement by Raw Waveform-mapping using Fully Convolutional Networks
Chang-Le Liu, Sze-Wei Fu, You-Jin Li +3
In recent years, waveform-mapping-based speech enhancement (SE) methods have garnered significant attention. These methods generally use a deep learning model to directly process a…
Increasing Compactness Of Deep Learning Based Speech Enhancement Models With Parameter Pruning And Quantization Techniques
Jyun-Yi Wu, Cheng Yu, Szu-Wei Fu +3
Most recent studies on deep learning based speech enhancement (SE) focused on improving denoising performance. However, successful SE applications require striking a desirable bala…
Generalization of Spectrum Differential based Direct Waveform Modification for Voice Conversion
Wen-Chin Huang, Yi-Chiao Wu, Kazuhiro Kobayashi +6
We present a modification to the spectrum differential based direct waveform modification for voice conversion (DIFFVC) so that it can be directly applied as a waveform generation…
MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement
Szu-Wei Fu, Chien-Feng Liao, Yu Tsao +1
Adversarial loss in a conditional generative adversarial network (GAN) is not designed to directly optimize evaluation metrics of a target task, and thus, may not always guide the…
Learning with Learned Loss Function: Speech Enhancement with Quality-Net to Improve Perceptual Evaluation of Speech Quality
Szu-Wei Fu, Chien-Feng Liao, Yu Tsao
Utilizing a human-perception-related objective function to train a speech enhancement model has become a popular topic recently. The main reason is that the conventional mean squar…
Investigation of F0 conditioning and Fully Convolutional Networks in Variational Autoencoder based Voice Conversion
Wen-Chin Huang, Yi-Chiao Wu, Chen-Chou Lo +6
In this work, we investigate the effectiveness of two techniques for improving variational autoencoder (VAE) based voice conversion (VC). First, we reconsider the relationship betw…
Incorporating Symbolic Sequential Modeling for Speech Enhancement
Chien-Feng Liao, Yu Tsao, Xugang Lu +1
In a noisy environment, a lossy speech signal can be automatically restored by a listener if he/she knows the language well. That is, with the built-in knowledge of a "language mod…
MOSNet: Deep Learning based Objective Assessment for Voice Conversion
Chen-Chou Lo, Szu-Wei Fu, Wen-Chin Huang +4
Existing objective evaluation metrics for voice conversion (VC) are not always correlated with human perception. Therefore, training VC models with such criteria may not effectivel…