State Tying forContext Dependent Phoneme Models.ppt

State Tying for Context Dependent Phoneme Models Reference J. J. Odell, The Use of Context in Large Vocabulary Speech Recognition, Ph.D. Thesis, Cambridge University, Cambridge, March 1995. 羅應順,自發性中文語音基本辨認系統之建立,交大電信,94.6 HTK books -HHed 1.Introduction In this paper several modifications of two well known methods for parameter reduction of Hidden Markov Models by state tying are described. The two methods are： A data driven method which clusters triphone states with a bottom up algorithm. A top down method which grows decision trees for triphone states . 1.Introduction (cont.) We investigate the following aspects： The possible reduction of the word error rate by state tying the consequences of different distance measures for the data driven approach and modifications of the original decision tree approach such as node merging Corpus Test corpus : 5000 word vocabulary of the WSJ November 92 task Evaluation corpus : 3000 word vocabulary of the VERBMOBIL ’95 task The reduction of the word error rate by state tying ： 14% for the WSJ task 5% for the VERBMOBIL task compared to simple triphone models. 2.State tying Aim： Reduce the number of parameters of the speech recognition system without a significant degradation in modeling accuracy. Steps: Establish triphone list of the training corpus Estimate the mean and variance of the triphone state by using a segmentation of the training data The triphone states are then subdivided into subsets according to their central phoneme and their position within the phoneme model. Inside these sets the states are tied together according to a distance measure. Additionally it has to be assured that every model contains a sufficient amount of training data. 3.Data driven method Two steps The triphone states being very much alike due to a distance measure are clustered together (seen = 50 times in the training corpus) The states which do not contain enough data are clustered together with the nearest neighbor Drawback For tr