Non-Stationary Pattern Recognition
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So far, we have discussed pattern recognition for stationary signals. In this chapter, we will discuss pattern recognition for both stationary and nonstationary signals. In speaker authentication, some tasks, such as speaker identification, are treated as stationary pattern recognition while others, such as speaker verification, are treated as non-stationary pattern recognition. We will introduce the stochastic modeling approach for both stationary and nonstationary pattern recognition. We will also introduce the Gaussian mixture model (GMM) and the hidden Markov model (HMM), two popular models that will be used throughout the book.
KeywordsHide Markov Model Speech Signal Gaussian Mixture Model Equal Error Rate Speaker Recognition
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