117 citations · 286 across the 56 of their papers we have counts for
21 papers · 1 filter
Unsupervised neural adaptation model based on optimal transport for spoken language identification
Xugang Lu, Peng Shen, Yu Tsao +1
Due to the mismatch of statistical distributions of acoustic speech between training and testing sets, the performance of spoken language identification (SLID) could be drastically…
Blind Monaural Source Separation on Heart and Lung Sounds Based on Periodic-Coded Deep Autoencoder
Kun-Hsi Tsai, Wei-Chien Wang, Chui-Hsuan Cheng +6
Auscultation is the most efficient way to diagnose cardiovascular and respiratory diseases. To reach accurate diagnoses, a device must be able to recognize heart and lung sounds fr…
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…
One Shot Learning for Speech Separation
Yuan-Kuei Wu, Kuan-Po Huang, Yu Tsao +1
Despite the recent success of speech separation models, they fail to separate sources properly while facing different sets of people or noisy environments. To tackle this problem,…
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…