Unsupervised Representation Learning with Future Observation Prediction for Speech Emotion Recognition
This work addresses low-resource speech emotion recognition by improving unsupervised learning, but it is incremental as it builds on existing strategies like FOP and transfer learning.
The paper tackled the problem of modeling long-term dynamic dependencies in speech emotion recognition by combining Future Observation Prediction with transfer learning, achieving superior performance over advanced unsupervised methods on the IEMOCAP database.
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.