Assessment of self-organizing map variants for clustering with application to redistribution of emotional speech patterns

Two well-known variants of the self-organizing map (SOM) that are based on multivariate order statistics are the marginal median SOM and the vector median SOM. In the past, their efficiency was demonstrated for color image quantization. We employ the well-known IRIS and VOWEL data sets and we assess the SOM variants’ performance with respect to the accuracy, the average over all neurons mean squared error between the patterns that were assigned to a neuron and the neuron's weight vector, the Rand index, the ΓΓ statistic, and the overall entropy. All figures of merit favor the marginal median SOM and the vector median SOM against the standard SOM. Based on the aforementioned findings, the marginal median SOM and the vector median SOM are used to redistribute emotional speech patterns from the Danish Emotional Speech database, which were originally classified as being neutral, to the emotional states of hot anger, happiness, sadness, and surprise.