| Revisiting hidden Markov models for speech emotion recognition S Mao, D Tao, G Zhang, PC Ching, T Lee ICASSP 2019-2019 IEEE international conference on acoustics, speech and …, 2019 | 81 | 2019 |
| Deep Learning of Segment-Level Feature Representation with Multiple Instance Learning for Utterance-Level Speech Emotion Recognition. S Mao, PC Ching, T Lee Interspeech, 1686-1690, 2019 | 38 | 2019 |
| Enhancing segment-based speech emotion recognition by iterative self-learning S Mao, PC Ching, T Lee IEEE/ACM Transactions on Audio, Speech, and Language Processing 30, 123-134, 2021 | 17 | 2021 |
| Advancing Multiple Instance Learning with Attention Modeling for Categorical Speech Emotion Recognition S Mao, PC Ching, T Lee INTERSPEECH, 2357-2361, 2020 | 13 | 2020 |
| EigenEmo: Spectral Utterance Representation Using Dynamic Mode Decomposition for Speech Emotion Classification S Mao, PC Ching, T Lee INTERSPEECH, 2352-2356, 2020 | 5 | 2020 |
| An effective discriminative learning approach for emotion-specific features using deep neural networks S Mao, PC Ching Neural Information Processing: 25th International Conference, ICONIP 2018 …, 2018 | 5 | 2018 |
| Emotion Profile Refinery for Speech Emotion Classification S Mao, PC Ching, T Lee INTERSPEECH, 531-535, 2020 | 4 | 2020 |
| Towards Training a Robust Segment-based Model for Speech Emotion Recognition S Mao The Chinese University of Hong Kong (Hong Kong), 2021 | | 2021 |
Tan Lee 李丹Department of Electronic Engineering, The Chinese University of Hong KongVerified email at ee.cuhk.edu.hk
