15 citations · 26 across the 11 of their papers we have counts for
11 papers
Federated Learning with Partially Labeled Data: A Conditional Distillation Approach
Pochuan Wang, Chen Shen, Masahiro Oda +4
In medical imaging, developing generalized segmentation models that can handle multiple organs and lesions is crucial. However, the scarcity of fully annotated datasets and strict…
A Study on Incorporating Whisper for Robust Speech Assessment
Ryandhimas E. Zezario, Yu-Wen Chen, Szu-Wei Fu +3
This research introduces an enhanced version of the multi-objective speech assessment model--MOSA-Net+, by leveraging the acoustic features from Whisper, a large-scaled weakly supe…
Non-Intrusive Speech Intelligibility Prediction for Hearing Aids using Whisper and Metadata
Ryandhimas E. Zezario, Fei Chen, Chiou-Shann Fuh +2
Automated speech intelligibility assessment is pivotal for hearing aid (HA) development. In this paper, we present three novel methods to improve intelligibility prediction accurac…
ConDistFL: Conditional Distillation for Federated Learning from Partially Annotated Data
Pochuan Wang, Chen Shen, Weichung Wang +4
Developing a generalized segmentation model capable of simultaneously delineating multiple organs and diseases is highly desirable. Federated learning (FL) is a key technology enab…
Multi-Task Pseudo-Label Learning for Non-Intrusive Speech Quality Assessment Model
Ryandhimas E. Zezario, Bo-Ren Brian Bai, Chiou-Shann Fuh +2
This study proposes a multi-task pseudo-label learning (MPL)-based non-intrusive speech quality assessment model called MTQ-Net. MPL consists of two stages: obtaining pseudo-label…
MTI-Net: A Multi-Target Speech Intelligibility Prediction Model
Ryandhimas E. Zezario, Szu-wei Fu, Fei Chen +3
Recently, deep learning (DL)-based non-intrusive speech assessment models have attracted great attention. Many studies report that these DL-based models yield satisfactory assessme…
MBI-Net: A Non-Intrusive Multi-Branched Speech Intelligibility Prediction Model for Hearing Aids
Ryandhimas E. Zezario, Fei Chen, Chiou-Shann Fuh +2
Improving the user's hearing ability to understand speech in noisy environments is critical to the development of hearing aid (HA) devices. For this, it is important to derive a me…
Deep Learning-based Non-Intrusive Multi-Objective Speech Assessment Model with Cross-Domain Features
Ryandhimas E. Zezario, Szu-Wei Fu, Fei Chen +3
In this study, we propose a cross-domain multi-objective speech assessment model called MOSA-Net, which can estimate multiple speech assessment metrics simultaneously. Experimental…
Multi-task Federated Learning for Heterogeneous Pancreas Segmentation
Chen Shen, Pochuan Wang, Holger R. Roth +9
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…
Speech Enhancement with Zero-Shot Model Selection
Ryandhimas E. Zezario, Chiou-Shann Fuh, Hsin-Min Wang +1
Recent research on speech enhancement (SE) has seen the emergence of deep-learning-based methods. It is still a challenging task to determine the effective ways to increase the gen…
STOI-Net: A Deep Learning based Non-Intrusive Speech Intelligibility Assessment Model
Ryandhimas E. Zezario, Szu-Wei Fu, Chiou-Shann Fuh +2
The calculation of most objective speech intelligibility assessment metrics requires clean speech as a reference. Such a requirement may limit the applicability of these metrics in…