477 citations · 1.7k across the 84 of their papers we have counts for
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A Unified Framework for Speech Separation
Fahimeh Bahmaninezhad, Shi-Xiong Zhang, Yong Xu +3
Speech separation refers to extracting each individual speech source in a given mixed signal. Recent advancements in speech separation and ongoing research in this area, have made…
Learning discriminative features in sequence training without requiring framewise labelled data
Jun Wang, Dan Su, Jie Chen +4
In this work, we try to answer two questions: Can deeply learned features with discriminative power benefit an ASR system's robustness to acoustic variability? And how to learn the…
Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network
Kun Xu, Liwei Wang, Mo Yu +4
Previous cross-lingual knowledge graph (KG) alignment studies rely on entity embeddings derived only from monolingual KG structural information, which may fail at matching entities…
A Comparison of Lattice-free Discriminative Training Criteria for Purely Sequence-Trained Neural Network Acoustic Models
Chao Weng, Dong Yu
In this work, three lattice-free (LF) discriminative training criteria for purely sequence-trained neural network acoustic models are compared on LVCSR tasks, namely maximum mutual…
Feature Learning in Deep Neural Networks - Studies on Speech Recognition Tasks
Dong Yu, Michael L. Seltzer, Jinyu Li +2
Recent studies have shown that deep neural networks (DNNs) perform significantly better than shallow networks and Gaussian mixture models (GMMs) on large vocabulary speech recognit…