In this paper, we present a class-based variable memory length Markov model and its learning algorithm. This is an extension of a variable memory length Markov model. Our model is based on a class-based probabilistic suffix tree, whose nodes have an automatically acquired word-class relation. We experimentally compared our new model with a word-based bi-gram model, a word-based tri-gram model, a class-based bi-gram model, and a word-based variable memory length Markov model. The results show that a class-based variable memory length Markov model outperforms the other models in perplexity and model size.
Cite as: Mori, S., Kurata, G. (2005) Class-based variable memory length Markov model. Proc. Interspeech 2005, 13-16, doi: 10.21437/Interspeech.2005-6
@inproceedings{mori05_interspeech,
author={Shinsuke Mori and Gakuto Kurata},
title={{Class-based variable memory length Markov model}},
year=2005,
booktitle={Proc. Interspeech 2005},
pages={13--16},
doi={10.21437/Interspeech.2005-6}
}