| Predicting Arousal and Valence from Waveforms and Spectrograms Using Deep Neural Networks. Z Yang, J Hirschberg Interspeech, 3092-3096, 2018 | 57 | 2018 |
| Improving text-to-text pre-trained models for the graph-to-text task Z Yang, A Einolghozati, H Inan, K Diedrick, A Fan, P Donmez, S Gupta Proceedings of the 3rd International Workshop on Natural Language Generation …, 2020 | 24 | 2020 |
| What makes a speaker charismatic? producing and perceiving charismatic speech Z Yang, J Huynh, R Tabata, N Cestero, T Aharoni, J Hirschberg Proc. 10th International Conference on Speech Prosody 2020, 685-689, 2020 | 19 | 2020 |
| Predicting Humor by Learning from Time-Aligned Comments. Z Yang, B Hu, J Hirschberg INTERSPEECH, 496-500, 2019 | 11 | 2019 |
| Linguistically-Informed Training of Acoustic Word Embeddings for Low-Resource Languages. Z Yang, J Hirschberg Interspeech, 2678-2682, 2019 | 10 | 2019 |
| Multimodal indicators of humor in videos Z Yang, L Ai, J Hirschberg 2019 IEEE Conference on Multimedia Information Processing and Retrieval …, 2019 | 10 | 2019 |
| CHoRaL: Collecting humor reaction labels from millions of social media users Z Yang, S Hooshmand, J Hirschberg Proceedings of the 2021 Conference on Empirical Methods in Natural Language …, 2021 | 8 | 2021 |
| OS-ELM based emotion recognition for empathetic elderly companion Z Yang, Q Wu, C Leung, C Miao Proceedings of ELM-2014 Volume 2: Applications, 331-341, 2015 | 5 | 2015 |
| Exploring New Methods for Identifying False Information and the Intent Behind It on Social Media: COVID-19 Tweets. L Ai, R Chen, Z Gong, J Guo, S Hooshmand, Z Yang, J Hirschberg ICWSM Workshops, 2021 | 1 | 2021 |
| Identifying Speaker State from Multimodal Cues Z Yang Columbia University, 2021 | | 2021 |
