56 citations · 102 across the 14 of their papers we have counts for
15 papers
SEAL: Mixture-Closed Additive Reconstruction and Refinement-Aware Expert Routing for Efficient Speech Separation
Shao-Chun Hu, Zi-Xiang Lin, Jeih-Weih Hung +1
Compact time-frequency separators that mask the mixture and refine through a shared cell face two limits. First, a bounded multiplicative mask only scales a mixture bin, so where o…
MALEFA: Multi-grAnularity Learning and Effective False Alarm Suppression for Zero-shot Keyword Spotting
Lo-Ya Li, Tien-Hong Lo, Jeih-Weih Hung +2
User-defined keyword spotting (KWS) without resorting to domain-specific pre-labeled training data is of fundamental importance in building adaptable and personalized voice interfa…
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…