A continuous density neural tree network word spotting system

Abstract

A new classifier is described that combines the discriminatory ability of the neural tree network (NTN) with the Gaussian mixture model to create a continuous density neural tree network (CDNTN). The CDNTN is used within a hidden Markov model (HMM), along with a nonparametric state duration model to construct a continuous word spotting system for real time applications. The new word spotting system does not use a general background model, allowing construction of independent models whose performance is independent of the number of models in the recognition system, supporting a direct parallel implementation. Although HMM word spotting systems are shown to provide good performance when sufficient training data is available, for applications where background speech data is not available or only a limited numbers of training tokens are available, the CDNTN word spotting system is shown to out perform comparable HMM systems.