56 citations · 84 across the 19 of their papers we have counts for
19 papers
Study on the Correlation between Objective Evaluations and Subjective Speech Quality and Intelligibility
Hsin-Tien Chiang, Kuo-Hsuan Hung, Szu-Wei Fu +3
Subjective tests are the gold standard for evaluating speech quality and intelligibility; however, they are time-consuming and expensive. Thus, objective measures that align with h…
PreFallKD: Pre-Impact Fall Detection via CNN-ViT Knowledge Distillation
Tin-Han Chi, Kai-Chun Liu, Chia-Yeh Hsieh +2
Fall accidents are critical issues in an aging and aged society. Recently, many researchers developed pre-impact fall detection systems using deep learning to support wearable-base…
Self-supervised learning-based general laboratory progress pretrained model for cardiovascular event detection
Li-Chin Chen, Kuo-Hsuan Hung, Yi-Ju Tseng +4
The inherent nature of patient data poses several challenges. Prevalent cases amass substantial longitudinal data owing to their patient volume and consistent follow-ups, however,…
Interpretations of Domain Adaptations via Layer Variational Analysis
Huan-Hsin Tseng, Hsin-Yi Lin, Kuo-Hsuan Hung +1
Transfer learning is known to perform efficiently in many applications empirically, yet limited literature reports the mechanism behind the scene. This study establishes both forma…
On the robustness of non-intrusive speech quality model by adversarial examples
Hsin-Yi Lin, Huan-Hsin Tseng, Yu Tsao
It has been shown recently that deep learning based models are effective on speech quality prediction and could outperform traditional metrics in various perspectives. Although net…
Inference and Denoise: Causal Inference-based Neural Speech Enhancement
Tsun-An Hsieh, Chao-Han Huck Yang, Pin-Yu Chen +2
This study addresses the speech enhancement (SE) task within the causal inference paradigm by modeling the noise presence as an intervention. Based on the potential outcome framewo…
ECG Artifact Removal from Single-Channel Surface EMG Using Fully Convolutional Networks
Kuan-Chen Wang, Kai-Chun Liu, Sheng-Yu Peng +1
Electrocardiogram (ECG) artifact contamination often occurs in surface electromyography (sEMG) applications when the measured muscles are in proximity to the heart. Previous studie…
Audio-Visual Speech Enhancement and Separation by Utilizing Multi-Modal Self-Supervised Embeddings
I-Chun Chern, Kuo-Hsuan Hung, Yi-Ting Chen +5
AV-HuBERT, a multi-modal self-supervised learning model, has been shown to be effective for categorical problems such as automatic speech recognition and lip-reading. This suggests…
Boosting Self-Supervised Embeddings for Speech Enhancement
Kuo-Hsuan Hung, Szu-wei Fu, Huan-Hsin Tseng +3
Self-supervised learning (SSL) representation for speech has achieved state-of-the-art (SOTA) performance on several downstream tasks. However, there remains room for improvement i…
When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computing
Chao-Han Huck Yang, Jun Qi, Samuel Yen-Chi Chen +2
The rapid development of quantum computing has demonstrated many unique characteristics of quantum advantages, such as richer feature representation and more secured protection on…
Partially Fake Audio Detection by Self-attention-based Fake Span Discovery
Haibin Wu, Heng-Cheng Kuo, Naijun Zheng +5
The past few years have witnessed the significant advances of speech synthesis and voice conversion technologies. However, such technologies can undermine the robustness of broadly…
Toward Real-World Voice Disorder Classification
Heng-Cheng Kuo, Yu-Peng Hsieh, Huan-Hsin Tseng +3
Objective: Voice disorders significantly compromise individuals' ability to speak in their daily lives. Without early diagnosis and treatment, these disorders may deteriorate drast…
SEOFP-NET: Compression and Acceleration of Deep Neural Networks for Speech Enhancement Using Sign-Exponent-Only Floating-Points
Yu-Chen Lin, Cheng Yu, Yi-Te Hsu +3
Numerous compression and acceleration strategies have achieved outstanding results on classification tasks in various fields, such as computer vision and speech signal processing.…
Neural Model Reprogramming with Similarity Based Mapping for Low-Resource Spoken Command Recognition
Hao Yen, Pin-Jui Ku, Chao-Han Huck Yang +4
In this study, we propose a novel adversarial reprogramming (AR) approach for low-resource spoken command recognition (SCR), and build an AR-SCR system. The AR procedure aims to mo…
Speech Recovery for Real-World Self-powered Intermittent Devices
Yu-Chen Lin, Tsun-An Hsieh, Kuo-Hsuan Hung +4
The incompleteness of speech inputs severely degrades the performance of all the related speech signal processing applications. Although many researches have been proposed to addre…
Domain-adaptive Fall Detection Using Deep Adversarial Training
Kai-Chun Liu, Michael Can, Heng-Cheng Kuo +4
Fall detection (FD) systems are important assistive technologies for healthcare that can detect emergency fall events and alert caregivers. However, it is not easy to obtain large-…
SRECG: ECG Signal Super-resolution Framework for Portable/Wearable Devices in Cardiac Arrhythmias Classification
Tsai-Min Chen, Yuan-Hong Tsai, Huan-Hsin Tseng +6
A combination of cloud-based deep learning (DL) algorithms with portable/wearable (P/W) devices has been developed as a smart heath care system to support automatic cardiac arrhyth…
Deep Learning Based Signal Enhancement of Low-Resolution Accelerometer for Fall Detection Systems
Kai-Chun Liu, Kuo-Hsuan Hung, Chia-Yeh Hsieh +3
In the last two decades, fall detection (FD) systems have been developed as a popular assistive technology. Such systems automatically detect critical fall events and immediately a…
Using Deep Learning and Explainable Artificial Intelligence in Patients' Choices of Hospital Levels
Lichin Chen, Yu Tsao, Ji-Tian Sheu
In countries that enabled patients to choose their own providers, a common problem is that the patients did not make rational decisions, and hence, fail to use healthcare resources…