Speech dimensionality analysis on hypercubical self-organizing maps
The problem of finding the intrinsic dimension of speech is addressed in this paper. A structured 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.
self-organizing map dimension reduction fractal dimension multi-dimensional scaling sequence projection speech analysis