A Novel Isolated Speech Recognition Method Based on Neural Network

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 220)

Abstract

The Radial Basis Function Neural Network architecture has been shown to be suitable for the recognition of isolated words. Recognition of words is carried out in speaker-dependent mode. In this mode the tested data presented to the network are the same as the trained data. The 16 Linear Predictive Cepstral Coefficients with 16 parameters from each frame improves a good feature extraction method for the spoken words, since the first 16 in the cepstrum represent most of the formant information. It is found that the performance of radial basis function neural network (RBF) classifier is superior to MLP classifier. It is found that speaker 6 average performances is the best performance in training MLP classifier and speaker 2 average performances is the best performance in training RBF classifier. It is found that average speaker 4 performances is the best performance in testing MLP classifier and speaker 1 average performance is the best performance in testing RBF classifier.

Keywords

Neural network Speech recognition Multilayer perception 

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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.Information and Control Engineering Institute, Shenyang Jianzhu UniversityShenyangChina

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