56 citations · 141 across the 21 of their papers we have counts for
15 papers · 1 filter
RRP-Voice: A Longitudinal Dataset and Benchmark for Recurrent Respiratory Papillomatosis Detection
Wenze Ren, Ke-Han Lu, Kai-Wei Chang +8
Deep learning has advanced pathological voice detection rapidly, yet rare laryngeal diseases remain underexplored due to data scarcity. Recurrent Respiratory Papillomatosis (RRP) e…
Unsupervised Face-Masked Speech Enhancement Using Generative Adversarial Networks With Human-in-the-Loop Assessment Metrics
Syu-Siang Wang, Jia-Yang Chen, Bo-Ren Bai +2
The utilization of face masks is an essential healthcare measure, particularly during times of pandemics, yet it can present challenges in communication in our daily lives. To addr…
IANS: Intelligibility-aware Null-steering Beamforming for Dual-Microphone Arrays
Wen-Yuan Ting, Syu-Siang Wang, Yu Tsao +1
Beamforming techniques are popular in speech-related applications due to their effective spatial filtering capabilities. Nonetheless, conventional beamforming techniques generally…
Continuous Speech for Improved Learning Pathological Voice Disorders
Syu-Siang Wang, Chi-Te Wang, Chih-Chung Lai +2
Goal: Numerous studies had successfully differentiated normal and abnormal voice samples. Nevertheless, further classification had rarely been attempted. This study proposes a nove…
Speech Enhancement Based on Cyclegan with Noise-informed Training
Wen-Yuan Ting, Syu-Siang Wang, Hsin-Li Chang +2
Cycle-consistent generative adversarial networks (CycleGAN) were successfully applied to speech enhancement (SE) tasks with unpaired noisy-clean training data. The CycleGAN SE syst…
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…
Attention-based multi-task learning for speech-enhancement and speaker-identification in multi-speaker dialogue scenario
Chiang-Jen Peng, Yun-Ju Chan, Cheng Yu +3
Multi-task learning (MTL) and attention mechanism have been proven to effectively extract robust acoustic features for various speech-related tasks in noisy environments. In this s…
CITISEN: A Deep Learning-Based Speech Signal-Processing Mobile Application
Yu-Wen Chen, Kuo-Hsuan Hung, You-Jin Li +6
This study presents a deep learning-based speech signal-processing mobile application known as CITISEN. The CITISEN provides three functions: speech enhancement (SE), model adaptat…
Boosting Objective Scores of a Speech Enhancement Model by MetricGAN Post-processing
Szu-Wei Fu, Chien-Feng Liao, Tsun-An Hsieh +9
The Transformer architecture has demonstrated a superior ability compared to recurrent neural networks in many different natural language processing applications. Therefore, our st…
MIMO Speech Compression and Enhancement Based on Convolutional Denoising Autoencoder
You-Jin Li, Syu-Siang Wang, Yu Tsao +1
For speech-related applications in IoT environments, identifying effective methods to handle interference noises and compress the amount of data in transmissions is essential to ac…
Speech Enhancement based on Denoising Autoencoder with Multi-branched Encoders
Cheng Yu, Ryandhimas E. Zezario, Syu-Siang Wang +5
Deep learning-based models have greatly advanced the performance of speech enhancement (SE) systems. However, two problems remain unsolved, which are closely related to model gener…
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…
Reinforcement Learning Based Speech Enhancement for Robust Speech Recognition
Yih-Liang Shen, Chao-Yuan Huang, Syu-Siang Wang +3
Conventional deep neural network (DNN)-based speech enhancement (SE) approaches aim to minimize the mean square error (MSE) between enhanced speech and clean reference. The MSE-opt…
Speech Enhancement Based on Reducing the Detail Portion of Speech Spectrograms in Modulation Domain via Discrete Wavelet Transform
Shih-kuang Lee, Syu-Siang Wang, Yu Tsao +1
In this paper, we propose a novel speech enhancement (SE) method by exploiting the discrete wavelet transform (DWT). This new method reduces the amount of fast time-varying portion…