# Unsupervised Representation Learning with Future Observation Prediction for Speech Emotion Recognition

arXiv: https://arxiv.org/abs/1910.13806 · HTML: https://mlsanity.com/paper/1910.13806/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2019.

## Built on by (most cited first, 4 of 4)

- [MER 2023: Multi-label Learning, Modality Robustness, and Semi-Supervised Learning](https://mlsanity.com/paper/2304.08981/) (2023)
- [Contrastive Unsupervised Learning for Speech Emotion Recognition](https://mlsanity.com/paper/2102.06357/) (2021)
- [Learning Speech Emotion Representations in the Quaternion Domain](https://mlsanity.com/paper/2204.02385/) (2022)
- [From Physics to Representation: Audio Learning with Synthetic Pre-training via Procedural Generation](https://mlsanity.com/paper/2606.14791/) (2026)

## Abstract (arXiv)

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.