| Deep convolutional recurrent neural network with attention mechanism for robust speech emotion recognition CW Huang, SS Narayanan 2017 IEEE international conference on multimedia and expo (ICME), 583-588, 2017 | 130 | 2017 |
| Attention assisted discovery of sub-utterance structure in speech emotion recognition. CW Huang, SS Narayanan Interspeech, 1387-1391, 2016 | 111 | 2016 |
| Efficient minimum word error rate training of RNN-transducer for end-to-end speech recognition J Guo, G Tiwari, J Droppo, M Van Segbroeck, CW Huang, A Stolcke, ... arXiv preprint arXiv:2007.13802, 2020 | 53 | 2020 |
| Wav2vec-c: A self-supervised model for speech representation learning S Sadhu, D He, CW Huang, SH Mallidi, M Wu, A Rastrow, A Stolcke, ... arXiv preprint arXiv:2103.08393, 2021 | 51 | 2021 |
| A technology prototype system for rating therapist empathy from audio recordings in addiction counseling B Xiao, C Huang, ZE Imel, DC Atkins, P Georgiou, SS Narayanan PeerJ Computer Science 2, e59, 2016 | 51 | 2016 |
| Characterizing types of convolution in deep convolutional recurrent neural networks for robust speech emotion recognition CW Huang, S Narayanan arXiv preprint arXiv:1706.02901, 2017 | 37 | 2017 |
| Gender representation in cinematic content: A multimodal approach T Guha, CW Huang, N Kumar, Y Zhu, SS Narayanan Proceedings of the 2015 ACM on International Conference on Multimodal …, 2015 | 26 | 2015 |
| A Study for Improving Device-Directed Speech Detection toward Frictionless Human-Machine Interaction CW Haung, R Maas, SH Mallidi, B Hoffmeister Proceedings of Interspeech, 2019 | 23 | 2019 |
| Flow of renyi information in deep neural networks CW Huang, SSS Narayanan 2016 IEEE 26th international workshop on machine learning for signal …, 2016 | 11 | 2016 |
| Comparison of feature-level and kernel-level data fusion methods in multi-sensory fall detection CW Huang, S Narayanan 2016 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP …, 2016 | 10 | 2016 |
| Stochastic Shake-Shake Regularization for Affective Learning from Speech. CW Huang, SS Narayanan INTERSPEECH, 3658-3662, 2018 | 9 | 2018 |
| Shaking acoustic spectral sub-bands can Letxer regularize learning in affective computing CW Huang, S Narayanan 2018 IEEE International Conference on Acoustics, Speech and Signal …, 2018 | 8 | 2018 |
| Streaming ResLSTM with causal mean aggregation for device-directed utterance detection X Tong, CW Huang, SH Mallidi, S Joseph, S Pareek, C Chandak, ... 2021 IEEE Spoken Language Technology Workshop (SLT), 659-664, 2021 | 7 | 2021 |
| Novel affective features for multiscale prediction of emotion in music N Kumar, T Guha, CW Huang, C Vaz, SS Narayanan 2016 ieee 18th international workshop on multimedia signal processing (mmsp …, 2016 | 6 | 2016 |
| Unsupervised speaker diarization using riemannian manifold clustering CW Huang, B Xiao, PG Georgiou, SS Narayanan Fifteenth Annual Conference of the International Speech Communication …, 2014 | 6 | 2014 |
| Normalization before shaking toward learning symmetrically distributed representation without margin in speech emotion recognition CW Huang, SS Narayanan arXiv preprint arXiv:1808.00876, 2018 | 3 | 2018 |
| On Role and Location of Normalization Before Model-based Data Augmentation in Residual Blocks for Classification Tasks CW Huang, S Narayanan 2019 IEEE International Conference on Acoustics, Speech and Signal …, 2019 | | 2019 |
| Toward Robust Affective Learning from Speech Signals Based on Deep Learning Techniques CW Huang University of Southern California, 2018 | | 2018 |
| A quantum effect in the classical limit: nonequilibrium tunneling in the Duffing oscillator AMB Shu-Hao Yeh, Dong-Bang Tsai, Che-Wei Huang, Md. Manirul Ali https://arxiv.org/abs/1208.1701, 2012 | | 2012 |
