Sixth International Conference on Spoken Language Processing
October 16-20, 2000
Efficient Mixed-Order Hidden Markov Model Inference
Ludwig Schwardt, Johan du Preez
Department of Electrical and Electronic Engineering,
University of Stellenbosch, Matieland, South Africa
Recent studies have shown that high-order hidden Markov models
(HMMs) are feasible and useful for spoken language processing.
This paper extends the fixed-order versions to ergodic mixedorder
HMMs, which allow the modelling of variable-length contexts
with significantly less parameters. A novel training procedure
automatically infers the number of states and the topology
of the HMM from the training set, based on information-theoretic
criteria. This is done by incorporating only high-order contexts
with sufficient support in the data. The mixed-order training algorithm
is faster than fixed-order methods, with similar classifi-
cation performance in language identification tasks.
Schwardt, Ludwig / Preez, Johan du (2000):
"Efficient mixed-order hidden Markov model inference",
In ICSLP-2000, vol.2, 238-241.