Sashi Novitasari, Takashi Fukuda, et al.
INTERSPEECH 2025
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.
Sashi Novitasari, Takashi Fukuda, et al.
INTERSPEECH 2025
Michael Heck, Masayuki Suzuki, et al.
INTERSPEECH 2017
Miroslav Novak
INTERSPEECH - Eurospeech 2005
Etienne Marcheret, Karthik Visweswariah, et al.
INTERSPEECH - Eurospeech 2005