Unsupervised Representation Learning with Future Observation Prediction for Speech Emotion Recognition

Zheng Lian, Jianhua Tao, Bin Liu, Jian Huang


Prior works on speech emotion recognition utilize various unsupervised learning approaches to deal with low-resource samples. However, these methods pay less attention to modeling the long-term dynamic dependency, which is important for speech emotion recognition. To deal with this problem, this paper combines the unsupervised representation learning strategy — Future Observation Prediction (FOP), with transfer learning approaches (such as Fine-tuning and Hypercolumns). To verify the effectiveness of the proposed method, we conduct experiments on the IEMOCAP database. Experimental results demonstrate that our method is superior to currently advanced unsupervised learning strategies.


 DOI: 10.21437/Interspeech.2019-1582

Cite as: Lian, Z., Tao, J., Liu, B., Huang, J. (2019) Unsupervised Representation Learning with Future Observation Prediction for Speech Emotion Recognition. Proc. Interspeech 2019, 3840-3844, DOI: 10.21437/Interspeech.2019-1582.


@inproceedings{Lian2019,
  author={Zheng Lian and Jianhua Tao and Bin Liu and Jian Huang},
  title={{Unsupervised Representation Learning with Future Observation Prediction for Speech Emotion Recognition}},
  year=2019,
  booktitle={Proc. Interspeech 2019},
  pages={3840--3844},
  doi={10.21437/Interspeech.2019-1582},
  url={http://dx.doi.org/10.21437/Interspeech.2019-1582}
}