Abstract
The paper presents a probabilistic echo-state network (π-ESN) for density estimation over variable-length sequences of multivariate random vectors. The π-ESN stems from the combination of the reservoir of an ESN and a parametric density model based on radial basis functions. A constrained maximum likelihood training algorithm is introduced, suitable for sequence classification. Extensions of the algorithm to unsupervised clustering and semi-supervised learning (SSL) of sequences are proposed. Experiments in emotion recognition from speech signals are conducted on the WaSeP© dataset. Compared with established techniques, the π-ESN yields the highest recognition accuracies, and shows interesting clustering and SSL capabilities.
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