12 citations · 27 across the 6 of their papers we have counts for
6 papers
Improving Label Assignments Learning by Dynamic Sample Dropout Combined with Layer-wise Optimization in Speech Separation
Chenyang Gao, Yue Gu, Ivan Marsic
In supervised speech separation, permutation invariant training (PIT) is widely used to handle label ambiguity by selecting the best permutation to update the model. Despite its su…
Progressive Learning for Stabilizing Label Selection in Speech Separation with Mapping-based Method
Chenyang Gao, Yue Gu, Ivan Marsic
Speech separation has been studied in time domain because of lower latency and higher performance compared to time-frequency domain. The masking-based method has been mostly used i…
RHR-Net: A Residual Hourglass Recurrent Neural Network for Speech Enhancement
Jalal Abdulbaqi, Yue Gu, Ivan Marsic
Most current speech enhancement models use spectrogram features that require an expensive transformation and result in phase information loss. Previous work has overcome these issu…
Multimodal Affective Analysis Using Hierarchical Attention Strategy with Word-Level Alignment
Yue Gu, Kangning Yang, Shiyu Fu +3
Multimodal affective computing, learning to recognize and interpret human affects and subjective information from multiple data sources, is still challenging because: (i) it is har…
Deep Multimodal Learning for Emotion Recognition in Spoken Language
Yue Gu, Shuhong Chen, Ivan Marsic
In this paper, we present a novel deep multimodal framework to predict human emotions based on sentence-level spoken language. Our architecture has two distinctive characteristics.…
Progress Estimation and Phase Detection for Sequential Processes
Xinyu Li, Yanyi Zhang, Jianyu Zhang +7
Process modeling and understanding are fundamental for advanced human-computer interfaces and automation systems. Most recent research has focused on activity recognition, but litt…