Speech Dimensionality Analysis on Hypercubical Self-Organizing Maps

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.

DOI: 10.1023/A:1023646203167

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@article{Somervuo2003SpeechDA, title={Speech Dimensionality Analysis on Hypercubical Self-Organizing Maps}, author={Panu Somervuo}, journal={Neural Processing Letters}, year={2003}, volume={17}, pages={125-136} }