Thesis available: On the Analysis of Pattern Sequences by SOMs

[Jari Kangas]

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	      On the Analysis of Pattern Sequences
                     by Self-Organizing Maps

			  Jari Kangas

		        Dr.Tech. Thesis
               Helsinki University of Technology

Abstract: This thesis is organized in three parts. In the first part,
the Self-Organizing Map algorithm is introduced. The discussion focuses
on the analysis of the Self-Organizing Map algorithm. It is shown that
the nonlinear nature of the algorithm makes it difficult to analyze the
algorithm except in some trivial cases.

In the second part the Self-Organizing Map algorithm is applied
to several patterns sequence analysis tasks. The first application is
a voice quality analysis system.
It is shown that the Self-Organizing Map algorithm can be applied to
voice analysis by providing the visualization of certain deviations.
The key point in the applicability of Self-Organizing Map algorithm is
the topological nature of the mapping; similar voice samples are
mapped to nearby locations in the map.

The second application is a speech recognition system. Through several
experiments it is demonstrated that by collecting some time dependent
features and using them in conjunction with the basic Self-Organizing
Map algorithm one can improve the speech recognition accuracy considerably.

The applications explained in the second part of the thesis were rather
straightforward works where the sequential signal itself was transformed
for the analysis. In the third part of the thesis it is demonstrated
that the Self-Organizing Map algorithm itself could be extended by
identifying each Map unit with an arbitrary operator with capabilities
for pattern sequence processing. It is shown that the operator maps
are applicable for example to speech signal (waveform) categorization.


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The thesis is 86 pages (8 preamble + 78 text).

To obtain a copy of the Postscript file:

  % ftp vermis.hut.fi
  > Name: anonymous
  > Password: <Your email address>
  > cd pub/papers
  > binary
  > get kangas.thesis.ps.Z
       (The size of the compressed file is about 0.4Mbyte)
  > quit

Then:

  % uncompress kangas.thesis.ps.Z
        (The size of the uncompressed file is about 1.2Mbyte)
  % lpr -s -P<Printer-name> kangas.thesis.ps

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	Jari Kangas
	Helsinki University of Technology
	Neural Networks Research Centre
	Rakentajanaukio 2 C
	FIN-02150 Espoo, FINLAND