1 research outputs found
Unsupervised Representation Learning with Future Observation Prediction for Speech Emotion Recognition
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