Speaker dependent speech recognition training using simplified hidden markov modeling and robust end-point detection

Abstract

A speech recognition training system that provides for model generation to be used within speaker dependent speech recognition systems requiring very limited training data, including single token training. The present invention provides a very fast and reliable training method based on the segmentation of a speech signal for subsequent estimating of speaker dependent word models. In addition, the invention provides for a robust method of performing end-point detection of a word contained within a speech utterance or speech signal. The invention is geared ideally for speaker dependent speech recognition systems that employ word-based speaker dependent models. The invention provides the end-point detection method is operable to extract a desired word or phrase from a speech signal that is recorded in varying degrees of undesirable background noise. In addition, the invention provides a simplified method of building the speaker dependent models using a simplified hidden Markov modeling method. The invention requires very limited training and is operable within systems having constrained budgets of memory and processing resources. Some examples of candidate application areas include cellular telephones and other devices that would benefit from abbreviated training and have inherently limited memory and processing power.