If we knew what it was we were doing,
it would not be called research, would it? -- A. Einstein
Research interests
Self-Organizing Map (SOM) and
Learning Vector Quantization
(LVQ) algorithms and their applications in various practical problems.
Using SOMs and LVQ with
Hidden Markov Models (HMMs)
in speech recognition.
Thesis
In my Master's thesis
"Combinations of Learning Vector Quantization and
Self-Organizing Maps with hidden Markov models in speech recognition",
I present my studies in using SOMs and LVQ to train continuous
observation density HMMs for the phoneme recognition system of
the Laboratory of Information and Computer science.
The Licentiate's thesis
"Application of Learning Vector Quantization and
Self-Organizing Maps for training continuous density and
semicontinuous Markov models"
report the advanced studies around SOMs and LVQ with CDHMMs and SCHMMs.
My Doctor's thesis
"Using Self-Organizing Maps
and Learning Vector Quantization
for Mixture Density Hidden Markov Models"
is an introduction and collection of articles published in 1993 -
1997.
Articles
My published articles present some results from special
projects
in speech recognition and research to combine SOMs and LVQ with
continuous density HMMs
and
semi-continuous HMMs.
Also articles about
corrective tuning by LVQ
and
hybrid LVQ-Viterbi training
have been published.
Click here for the complete list of
my publications including also the new ones.
Annual Reports
1991,
1992,
1993,
1994,
1995,
Triennal 1994 - 1996:
part A,
part B,
Others
Our Research Staff
Mikko Kurimo <Mikko.Kurimo@hut.fi>
Last modified: Wed Feb 18 15:15:32 1998