1 code implementation • 22 Sep 2023 • Ryandhimas E. Zezario, Yu-Wen Chen, Szu-Wei Fu, Yu Tsao, Hsin-Min Wang, Chiou-Shann Fuh
The first part of this study investigates the correlation between the embedding features of Whisper and two self-supervised learning (SSL) models with subjective quality and intelligibility scores.
no code implementations • 18 Sep 2023 • Ryandhimas E. Zezario, Fei Chen, Chiou-Shann Fuh, Hsin-Min Wang, Yu Tsao
Automated assessment of speech intelligibility in hearing aid (HA) devices is of great importance.
no code implementations • 18 Aug 2023 • Ryandhimas E. Zezario, Bo-Ren Brian Bai, Chiou-Shann Fuh, Hsin-Min Wang, Yu Tsao
This study proposes a multi-task pseudo-label learning (MPL)-based non-intrusive speech quality assessment model called MTQ-Net.
1 code implementation • 8 Aug 2023 • Pochuan Wang, Chen Shen, Weichung Wang, Masahiro Oda, Chiou-Shann Fuh, Kensaku MORI, Holger R. Roth
Federated learning (FL) is a key technology enabling the collaborative development of a model without exchanging training data.
1 code implementation • ECCV 2022 2022 • Jhih-Ciang Wu*, He-Yen Hsieh*, Ding-Jie Chen, Chiou-Shann Fuh, Tyng-Luh Liu
Video anomaly detection (VAD) aims at localizing unexpected actions or activities in a video sequence.
Anomaly Detection In Surveillance Videos Self-Supervised Learning
no code implementations • 7 Apr 2022 • Ryandhimas E. Zezario, Szu-Wei Fu, Fei Chen, Chiou-Shann Fuh, Hsin-Min Wang, Yu Tsao
Recently, deep learning (DL)-based non-intrusive speech assessment models have attracted great attention.
no code implementations • 7 Apr 2022 • Ryandhimas E. Zezario, Fei Chen, Chiou-Shann Fuh, Hsin-Min Wang, Yu Tsao
In this study, we propose a multi-branched speech intelligibility prediction model (MBI-Net), for predicting the subjective intelligibility scores of HA users.
1 code implementation • 3 Nov 2021 • Ryandhimas E. Zezario, Szu-Wei Fu, Fei Chen, Chiou-Shann Fuh, Hsin-Min Wang, Yu Tsao
In this study, we propose a cross-domain multi-objective speech assessment model called MOSA-Net, which can estimate multiple speech assessment metrics simultaneously.
no code implementations • 19 Aug 2021 • Chen Shen, Pochuan Wang, Holger R. Roth, Dong Yang, Daguang Xu, Masahiro Oda, Weichung Wang, Chiou-Shann Fuh, Po-Ting Chen, Kao-Lang Liu, Wei-Chih Liao, Kensaku MORI
Federated learning (FL) for medical image segmentation becomes more challenging in multi-task settings where clients might have different categories of labels represented in their data.
no code implementations • ICCV 2021 • Jhih-Ciang Wu, Ding-Jie Chen, Chiou-Shann Fuh, Tyng-Luh Liu
Anomaly detection (AD) aims to address the task of classification or localization of image anomalies.
1 code implementation • 17 Dec 2020 • Ryandhimas E. Zezario, Chiou-Shann Fuh, Hsin-Min Wang, Yu Tsao
Experimental results confirmed that the proposed ZMOS approach can achieve better performance in both seen and unseen noise types compared to the baseline systems and other model selection systems, which indicates the effectiveness of the proposed approach in providing robust SE performance.
1 code implementation • 9 Nov 2020 • Ryandhimas E. Zezario, Szu-Wei Fu, Chiou-Shann Fuh, Yu Tsao, Hsin-Min Wang
To overcome this limitation, we propose a deep learning-based non-intrusive speech intelligibility assessment model, namely STOI-Net.
no code implementations • NeurIPS 2008 • Yen-Yu Lin, Tyng-Luh Liu, Chiou-Shann Fuh
In solving complex visual learning tasks, adopting multiple descriptors to more precisely characterize the data has been a feasible way for improving performance.