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ACII
2015
Springer

Continuous emotion recognition using a particle swarm optimized NARX neural network

3 years 10 days ago
Continuous emotion recognition using a particle swarm optimized NARX neural network
—The recognition of continuous dimensional emotion remains a challenging task due to large variations in the expression of emotion, and the difficulty of modeling emotion as temporal processes. This work proposes the use of a Nonlinear AutoRegressive with eXogenous inputs recurrent neural network (NARX-RNN) to learn emotional patterns in a given a dataset. The application of particle swarm optimisation in training the NARX-RNN is considered and compared to a gradient descent algorithm. We show that the NARX-RNN outperforms other methods in its emotion recognition ability, and can be easily trained with both gradient-free and gradient-based optimization methods.
Added 13 Apr 2016
Updated 13 Apr 2016
Type Journal
Year 2015
Where ACII
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