Speech Dimensionality Analysis on Hypercubical Self-Organizing Maps

Authors:
Panu Somervuo
Affiliations:
Helsinki University of Technology, Neural Networks Research Centre, P.O. Box 5400, FIN-02015 HUT, Finland. e-mail: panu.somervuo@hut.fi
Venue:
Neural Processing Letters
Year:
2003

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Abstract

The problem of finding the intrinsic dimension of speech is addressed in this paper. Astructured vector quantization lattice, Self-Organizing Map (SOM), is used as a projection space for the data. The goal is to find a hypercubical SOM lattice where the sequences of projected speech feature vectors form continuous trajectories. The effect of varying the dimension of the lattice is investigated using feature vector sequences computed from the TIMIT database.