  This paper investigates model merging, a technique for deriving Markov models
from text or speech corpora. Models are derived by starting with a large and
specific model and by successively combining states to build smaller and more
general models. We present methods to reduce the time complexity of the
algorithm and report on experiments on deriving language models for a speech
recognition task. The experiments show the advantage of model merging over the
standard bigram approach. The merged model assigns a lower perplexity to the
test set and uses considerably fewer states.
