Jianwu Dang
Papers
6
Total Citations
167
H-Index
5
About
Jianwu Dang is a leading researcher in speech emotion recognition and human-robot interaction, whose work bridges auditory perception and deep learning to make machines more emotionally intelligent. His core contributions lie in developing biologically inspired auditory front-ends and recurrent neural network architectures that capture the subtle temporal dynamics of human emotion in speech. Dang’s most influential work, a 2020 paper on speech emotion recognition using 3D convolutions and attention-based sliding recurrent networks, has garnered 83 citations and demonstrates how the human auditory system’s ability to track pitch and intensity can be emulated for robust emotion detection. He further advanced the field with multi-resolution modulation-filtered cochleagram features for LSTM-based dimensional emotion recognition (44 citations), enabling robots to track continuous emotional states rather than just discrete categories. Beyond speech, Dang has innovated in multimodal sensory fusion for robot self-localization using LSTM networks and explored dynamic brain connectivity during speech perception through EEG analysis. His recent work on multi-level attention mechanisms for categorical emotion recognition continues to push boundaries. With over 160 total citations across his publications, Dang’s research is essential reading for anyone interested in affective computing, auditory-inspired machine learning, and the future of natural human-robot communication.
Research Focus
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Top Papers
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