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Pattern Recognition for Speech Detection

  • E. S. Gopi
Chapter
Part of the Signals and Communication Technology book series (SCT)

Abstract

The supervised pattern recognition techniques such as back-propagation neural network (BPNN), support vector machine (SVM), hidden Markov model (HMM), and Gaussian mixture model (GMM) that are used to design the classifier for speech and speaker detection are described in this chapter. The unsupervised techniques such as fuzzy k-means algorithm and Kohonen self-organizing map (KSOM) are discussed in this chapter. The dimensionality reduction techniques such as principal component analysis (PCA), linear discriminant analysis (LDA), kernel LDA, and independent component analysis (ICA) are also discussed in this chapter. The techniques described in this chapter are illustrated using the MATLAB for better understanding.

Keywords

Support Vector Machine Hide Markov Model Linear Discriminant Analysis Independent Component Analysis Speech Signal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Electronics and Communication EngineeringNational Institute of Technology, TrichyTrichyIndia

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