56 citations · 110 across the 24 of their papers we have counts for
25 papers
Correlation-Guided Encoder Selection for Multi-Encoder Large Audio-Language Models
Pei-Jun Liao, Hung-Shin Lee, Wenze Ren +3
Multi-encoder fusion extends Large Audio-Language Models (LALMs) beyond speech-centric recognition, but selecting encoders via intuition or exhaustive search often introduces redun…
Leveraging Mamba with Full-Face Vision for Audio-Visual Speech Enhancement
Rong Chao, Wenze Ren, You-Jin Li +5
Recent Mamba-based models have shown promise in speech enhancement by efficiently modeling long-range temporal dependencies. However, models like Speech Enhancement Mamba (SEMamba)…
Linguistic Knowledge Transfer Learning for Speech Enhancement
Kuo-Hsuan Hung, Xugang Lu, Szu-Wei Fu +4
Linguistic knowledge plays a crucial role in spoken language comprehension. It provides essential semantic and syntactic context for speech perception in noisy environments. Howeve…
Robust Audio-Visual Speech Enhancement: Correcting Misassignments in Complex Environments with Advanced Post-Processing
Wenze Ren, Kuo-Hsuan Hung, Rong Chao +3
This paper addresses the prevalent issue of incorrect speech output in audio-visual speech enhancement (AVSE) systems, which is often caused by poor video quality and mismatched tr…
MECG-E: Mamba-based ECG Enhancer for Baseline Wander Removal
Kuo-Hsuan Hung, Kuan-Chen Wang, Kai-Chun Liu +4
Electrocardiogram (ECG) is an important non-invasive method for diagnosing cardiovascular disease. However, ECG signals are susceptible to noise contamination, such as electrical i…
Leveraging Joint Spectral and Spatial Learning with MAMBA for Multichannel Speech Enhancement
Wenze Ren, Haibin Wu, Yi-Cheng Lin +7
In multichannel speech enhancement, effectively capturing spatial and spectral information across different microphones is crucial for noise reduction. Traditional methods, such as…
Exploiting Consistency-Preserving Loss and Perceptual Contrast Stretching to Boost SSL-based Speech Enhancement
Muhammad Salman Khan, Moreno La Quatra, Kuo-Hsuan Hung +3
Self-supervised representation learning (SSL) has attained SOTA results on several downstream speech tasks, but SSL-based speech enhancement (SE) solutions still lag behind. To add…
Self-Supervised Speech Quality Estimation and Enhancement Using Only Clean Speech
Szu-Wei Fu, Kuo-Hsuan Hung, Yu Tsao +1
Speech quality estimation has recently undergone a paradigm shift from human-hearing expert designs to machine-learning models. However, current models rely mainly on supervised le…
Leveraging Self-Supervised Audio-Visual Pretrained Models to Improve Vocoded Speech Intelligibility in Cochlear Implant Simulation
Richard Lee Lai, Jen-Cheng Hou, I-Chun Chern +6
Individuals with hearing impairments face challenges in their ability to comprehend speech, particularly in noisy environments. The aim of this study is to explore the effectivenes…
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…
Preoperative Prognosis Assessment of Lumbar Spinal Surgery for Low Back Pain and Sciatica Patients based on Multimodalities and Multimodal Learning
Li-Chin Chen, Jung-Nien Lai, Hung-En Lin +3
Low back pain (LBP) and sciatica may require surgical therapy when they are symptomatic of severe pain. However, there is no effective measures to evaluate the surgical outcomes in…
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…
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…
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…
MetricGAN-U: Unsupervised speech enhancement/ dereverberation based only on noisy/ reverberated speech
Szu-Wei Fu, Cheng Yu, Kuo-Hsuan Hung +2
Most of the deep learning-based speech enhancement models are learned in a supervised manner, which implies that pairs of noisy and clean speech are required during training. Conse…
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…
EMA2S: An End-to-End Multimodal Articulatory-to-Speech System
Yu-Wen Chen, Kuo-Hsuan Hung, Shang-Yi Chuang +4
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. In…
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…
A Study of Incorporating Articulatory Movement Information in Speech Enhancement
Yu-Wen Chen, Kuo-Hsuan Hung, Shang-Yi Chuang +3
Although deep learning algorithms are widely used for improving speech enhancement (SE) performance, the performance remains limited under highly challenging conditions, such as un…
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…
Waveform-based Voice Activity Detection Exploiting Fully Convolutional networks with Multi-Branched Encoders
Cheng Yu, Kuo-Hsuan Hung, I-Fan Lin +3
In this study, we propose an encoder-decoder structured system with fully convolutional networks to implement voice activity detection (VAD) directly on the time-domain waveform. T…
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…