An improved method of speech recognition based on probabilistic neural network ensembles

Abstract

The neural network method is one of the most important methods in the field of speech recognition. In this paper, we propose a new speech recognition method, probabilistic neural network (PNN) ensembles, where the Bagging ensembles method is used to form a speech recognition model with probabilistic neural networks integrated, to implement a speaker-independent English speech recognition system. This paper also demonstrates that before speech recognition, applying segment clustering algorithm to the extracted speech data, i.e., the process of time warping, can ensure the validity of dataset and the performance of PNN. Through experiments, the experimental results show that the PNN ensembles method has faster modeling speed and higher recognition rate than the single BP (Back Propagation) and the BP ensembles method, and has higher recognition rate than the traditional PNN method.