Design of codebook using Centroid Neural Network with state dependence measure

Abstract

A codebook design method for Hidden Markov Model (HMM) by using a Centroid Neural Network (CNN) is applied to a Korean monophone recognition problem in this paper. In order to alleviate the accuracy degradation problem in tied mixture HMM (TMHMM), this paper utilizes a clustering algorithm, called Centroid Neural Network with State Dependence measure (CNN(SD)), for TMHMMs. The CNN(SD) uses a novel distance measure that discriminates between two observation densities in the HMM from the same state and those from different states. When compared with other conventional unsupervised neural networks, the CNN(SD) successfully allocates more code vectors to the regions where Gaussian Probability Density Function (GPDF) data of different states overlap each other while it allocates fewer code vectors to the regions where GPDF data are from the same states. Experiments and results on a Korean monophone data, the CNN(SD) shows improvements on the recognition accuracy over CNN and the traditional k-means algorithm.