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.
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© 2014 Springer India
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Gopi, E.S. (2014). Pattern Recognition for Speech Detection. In: Digital Speech Processing Using Matlab. Signals and Communication Technology. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1677-3_1
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DOI: https://doi.org/10.1007/978-81-322-1677-3_1
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Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-1676-6
Online ISBN: 978-81-322-1677-3
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