5 papers · 1 filter
SincQDR-VAD: A Noise-Robust Voice Activity Detection Framework Leveraging Learnable Filters and Ranking-Aware Optimization
Chien-Chun Wang, En-Lun Yu, Jeih-Weih Hung +2
Voice activity detection (VAD) is essential for speech-driven applications, but remains far from perfect in noisy and resource-limited environments. Existing methods often lack rob…
ConSep: a Noise- and Reverberation-Robust Speech Separation Framework by Magnitude Conditioning
Kuan-Hsun Ho, Jeih-weih Hung, Berlin Chen
Speech separation has recently made significant progress thanks to the fine-grained vision used in time-domain methods. However, several studies have shown that adopting Short-Time…
What do neural networks listen to? Exploring the crucial bands in Speech Enhancement using Sinc-convolution
Kuan-Hsun Ho, Jeih-weih Hung, Berlin Chen
This study introduces a reformed Sinc-convolution (Sincconv) framework tailored for the encoder component of deep networks for speech enhancement (SE). The reformed Sincconv, based…
Naaloss: Rethinking the objective of speech enhancement
Kuan-Hsun Ho, En-Lun Yu, Jeih-weih Hung +1
Reducing noise interference is crucial for automatic speech recognition (ASR) in a real-world scenario. However, most single-channel speech enhancement (SE) generates "processing a…
3M: An Effective Multi-view, Multi-granularity, and Multi-aspect Modeling Approach to English Pronunciation Assessment
Fu-An Chao, Tien-Hong Lo, Tzu-I Wu +2
As an indispensable ingredient of computer-assisted pronunciation training (CAPT), automatic pronunciation assessment (APA) plays a pivotal role in aiding self-directed language le…