Experimenting with a Global Decision Tree for State Clustering in Automatic Speech Recognition Systems

In modern automatic speech recognition systems, it is standard practice

to cluster several logical hidden Markov model states into one

physical, clustered state. Typically, the clustering is done such that

logical states from different phones or different states can not share

the same clustered state. In this paper, we present a collection of

experiments that lift this restriction. The results show that, for Aurora

2 and Aurora 3, much smaller models perform as least as well

as the standard baseline. On a TIMIT phone recognition task, we

analyze the tying structures introduced, and discuss the implications

for building better acoustic models.

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In  ICASSP 2009

Publisher  IEEE
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