AIRCC's International Journal of Computer Science and Information Technology

Open Access Open Access  Restricted Access Subscription Access

Novel Approach for Speech Recognition by Using Self - Organized Maps


Affiliations
1 Department of Information Technology, Sasi Institute of Technology and Engineering, Tadepalligudem, India
2 Perunthalaivar Kamarajar Arts College, Puducherry 605 107, India
 

The method of self-organizing maps (SOM) is a method of exploratory data analysis used for clustering and projecting multi-dimensional data into a lower-dimensional space to reveal hidden structure of the data. The Self-Organizing Feature Maps (SOFMs) [11] is a class of neural networks capable of recognizing the main features of the data they are trained on. There is extensive literature on its biological and mathematical concepts and even more on its implementation in a variety of areas including medicine, finance, chaos and data mining in general [4,2]. The aim of this research is to implement a self-organizing neural network based technique for speech recognition. The Mean-SOM performance for the feature Intensity is obtained maximum as 98.17%. The Median-SOM performance for the feature Intensity is obtained maximum as 98.54%.

Keywords

Self-Organized Map, Artificial Neural Networks, Feature, Mean-SOM Performance, Median-SOM Performance, LPCC, MFCC, Pitch, Intensity, Hits, Cycles, Iterations.
User
Notifications
Font Size

Abstract Views: 274

PDF Views: 196




  • Novel Approach for Speech Recognition by Using Self - Organized Maps

Abstract Views: 274  |  PDF Views: 196

Authors

R. L. K. Venkateswarlu
Department of Information Technology, Sasi Institute of Technology and Engineering, Tadepalligudem, India
R. Vasantha Kumari
Perunthalaivar Kamarajar Arts College, Puducherry 605 107, India
A. K. V. Nagayya
Department of Information Technology, Sasi Institute of Technology and Engineering, Tadepalligudem, India

Abstract


The method of self-organizing maps (SOM) is a method of exploratory data analysis used for clustering and projecting multi-dimensional data into a lower-dimensional space to reveal hidden structure of the data. The Self-Organizing Feature Maps (SOFMs) [11] is a class of neural networks capable of recognizing the main features of the data they are trained on. There is extensive literature on its biological and mathematical concepts and even more on its implementation in a variety of areas including medicine, finance, chaos and data mining in general [4,2]. The aim of this research is to implement a self-organizing neural network based technique for speech recognition. The Mean-SOM performance for the feature Intensity is obtained maximum as 98.17%. The Median-SOM performance for the feature Intensity is obtained maximum as 98.54%.

Keywords


Self-Organized Map, Artificial Neural Networks, Feature, Mean-SOM Performance, Median-SOM Performance, LPCC, MFCC, Pitch, Intensity, Hits, Cycles, Iterations.