13 citations · 27 across the 10 of their papers we have counts for
10 papers
Cleaner Speech, Weaker Generalization: Revisiting Pitt-Derived Benchmarks for Alzheimer's Disease Detection
Luqi Sun, Shreeram Suresh Chandra, Lin Zhang +5
Speech-based Alzheimer's disease (AD) detection increasingly relies on speech-enhanced and curated versions of the Pitt Corpus, where speech enhancement, sample selection, and demo…
Tracking Listener Attention: Gaze-Guided Audio-Visual Speech Enhancement Framework
Hsiang-Cheng Yang, You-Jin Li, Rong Chao +3
This paper presents a Gaze-Guided Audio-Visual Speech Enhancement (GG-AVSE) framework to address the cocktail party problem. A major challenge in conventional AVSE is identifying t…
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)…
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…
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…
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…
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…
Boundary-Preserved Deep Denoising of the Stochastic Resonance Enhanced Multiphoton Images
Sheng-Yong Niu, Lun-Zhang Guo, Yue Li +3
As the rapid growth of high-speed and deep-tissue imaging in biomedical research, it is urgent to find a robust and effective denoising method to retain morphological features for…