A comparative study of methods for phonetic decision-tree state clustering
Harriet J. Nock, Mark Gales, Steve J. Young · 1997
Phonetic decision trees have been widely used for obtaining robust context-dependent models in HMM-based systems. There are five key issues to consider when constructing phonetic decision trees: the alignment of data with the chosen phone classes; the quality of the modeling of the underlying data; the choice of partitioning method at each node; the goodness-of-split criterion and the method for determining appropriate tree sizes. A popular existing method usesefficient but crude approximatemethods for each of these. This paper introduces and evaluates more detailed alternatives to the standard approximations. 1. Introduction A key problem in building continuous-density Hidden Markov Model (HMM)-based context-dependent acoustic models is maintaining a balancebetween the desired model complexity and the number of parameters which can be robustly estimated from the available training data. One solution which has proved successful (eg.[8], [1]) is based upon the use of phonetic decision...