Speech recognition with a competitive Probabilistic Radial Basis Neural Network
Nima Yousefian, Azarakhsh Jalalvand, Pooyan Ahmadi, Morteza Analoui · 2008
Automatic speech recognition (ASR) has been a subject of active research in the last few decades. In this paper we study the applicability of a special model of radial basis probabilistic neural networks (RBPNN) as a classifier for speech recognition. This type of network is a combination of radial basis function (RBF) and probabilistic neural network (PNN) that applies characteristics of both networks and finally uses a competitive function for computing final result. The proposed network has been tested on the voices of single digit numbers dataset and produced lower recognition error rate in comparison with other common pattern classifiers. All of classifiers use Mel-scale frequency cepstrum coefficients (MFCC) and a special type of perceptual linear predictive (PLP) as their features for classification. Results show that PLP features yield better recognition rate by considering noisy dataset.