Non-Stationary Pattern Recognition

  • Qi (Peter) LiEmail author
Part of the Signals and Communication Technology book series (SCT)


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.


Hide Markov Model Speech Signal Gaussian Mixture Model Equal Error Rate Speaker Recognition 
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.


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© Springer-Verlag Berlin Heidelberg  2012

Authors and Affiliations

  1. 1.Li Creative Technologies (LcT), IncFlorham ParkUSA

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