Vol. 1 No. 1 (2011): Vol 1, Iss 1, Year 2011
Articles

Featurematching by skpcawithunsupervisedalgorithmand maximum probability in speech recognition

Pavithra M
Department of Electronics and Instrumentation, Bharathiar University.
Chinnasamy G
Department of Electronics and Instrumentation, Bharathiar University.
Azha Periasamy
Department of Electronics and Instrumentation, Bharathiar University.
Published June 30, 2011
Keywords
  • Microfinance, women’s empowerment, Non Governmental Organization, Self Help groups.
How to Cite
M, P., G, C., & Periasamy, A. (2011). Featurematching by skpcawithunsupervisedalgorithmand maximum probability in speech recognition. Journal of Management and Science, 1(1), 9-13. https://doi.org/10.26524/jms.2011.2

Abstract

A Speech recognition system requires a combination of various techniques and algorithms, each of which performs a specific task for achieving the main goal of the system. Speech recognition performance can be enhanced by selecting the proper acoustic model. In this work, the feature extraction and matching is done by SKPCA with Unsupervised learning algorithm and maximum probability. SKPCA reduces the data maximization of the model. It represents a sparse solution for KPCA, because the original data can be reduced considering the weights, i.e., the weights show the vectors which most influence the maximization. Unsupervised learning algorithm is implemented to find the suitable representation of the labels and maximum probability is used to maximize the
normalized acoustic likelihood of the most likely state sequences of training data. The experimental results show the efficiency of SKPCA technique with the proposed approach and maximum probability produce the great performance in the speech recognition system.

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