Open AccessA New Distance Measure for a Variable‐Sized Acoustic Model Based on MDL Technique
Author(s) -
Cho HoonYoung,
Kim Sanghun
Publication year - 2010
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.10.1510.0062
Subject(s) - minimum description length , mixture model , measure (data warehouse) , hidden markov model , divergence (linguistics) , gaussian , binary tree , gaussian process , mathematics , algorithm , tree (set theory) , kullback–leibler divergence , bayesian information criterion , binary number , word error rate , embedding , pattern recognition (psychology) , computer science , artificial intelligence , statistics , data mining , mathematical analysis , linguistics , philosophy , physics , quantum mechanics , arithmetic
Embedding a large vocabulary speech recognition system in mobile devices requires a reduced acoustic model obtained by eliminating redundant model parameters. In conventional optimization methods based on the minimum description length (MDL) criterion, a binary Gaussian tree is built at each state of a hidden Markov model by iteratively finding and merging similar mixture components. An optimal subset of the tree nodes is then selected to generate a downsized acoustic model. To obtain a better binary Gaussian tree by improving the process of finding the most similar Gaussian components, this paper proposes a new distance measure that exploits the difference in likelihood values for cases before and after two components are combined. The mixture weight of Gaussian components is also introduced in the component merging step. Experimental results show that the proposed method outperforms MDL‐based optimization using either a Kullback‐Leibler (KL) divergence or weighted KL divergence measure. The proposed method could also reduce the acoustic model size by 50% with less than a 1.5% increase in error rate compared to a baseline system.