On the Search of Organization Measures for a Kohonen Map Case Study: Speech Signal Recognition

Unsupervised learning scheme like the self-organizing map (SOM) has been used to classify speech sounds in an ordered manner. SOM is able to extract the most salient features of the input signal and provides a simple way of visualizing them. The distance between two units on the map was used as an objective measure of their perceptual similarity. This paper presents a study of the evaluation of a SOM trained by a sequential learning algorithm integrating information enrichment principal. Two complementary analyses are proposed: quantitative analysis and qualitative one. SOM has been used to visualize speech database as a phenotypic map. The latter was used to generate quantitative measures of the input space. In this paper, we propose different organization measures of a Kohonen map. Some of them are restricted to evaluate a map without referring to data manifold. Others are restricted to quantify map organization with respect to data manifold. We propose also how these organization measures act as fitness measures. These organizations measures are evaluated on the case study of phoneme classification of TIMIT acoustic-phonetic continuous speech corpus. The experimental results show that the proposed combined organization measures provide more significant values according to map sizes.