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SPEECH RECOGNITION IN NEURO-FUZZY SYSTEM


Author(s):

PRANAVKUMAR N. CHAUDHARI , PARUL INSTITUTE OF ENGINEERING & TECHNOLOGY

Keywords:

Neural network, fuzzy system, Speech Recognition

Abstract:

Neural networks are excellent classifiers, but performance is dependent on the quality and quantity of training samples presented to the network. In cases where training data is sparse or not fully representative of the range of values possible, incorporation of fuzzy techniques improves performance. That is, introducing fuzzy techniques allow the classification of imprecise data. The neuro-fuzzy system presented in this study is a neural network that processes fuzzy numbers. By incorporating this attribute, the system acquires the capacity to correctly classify imprecise input. Experimental results show that the neuro-fuzzy system’s performance is vastly improved over a standard neural network for speaker-independent speech recognition. Speaker indepen-dent speech recognition is a particularly difficult classification problem, due to differences in voice frequency (amongst speakers) and variations in pronunciation. The network developed in this study has an improvement of 45% over the original multi-layer perceptron used in a previous study.


Other Details:

Manuscript Id :IJSTEV1I1008
Published in :Volume : 1, Issue : 1
Publication Date: 01/07/2014
Page(s): 18-21
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