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Linear Prediction and ARMA Spectrum Estimation

  • Sean A. FulopEmail author
Chapter
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Part of the Signals and Communication Technology book series (SCT)

Abstract

Up to this point, I have presented speech analysis methods which obtain spectral or time–frequency information from the signal data directly by means of some kind of transformation. It has been shown how different processing schemes can extract such information from the signal in different ways, but none has relied on any special assumptions about a speech signal beyond the most general and widely-held sort. In this chapter, I introduce an entirely different approach to what is often called “spectral estimation,” in which the signal is explicitly assumed to conform to the outlines of a model. The parameters of the assumed model are then estimated from the signal data, and the values of the parameters are used as a kind of proxy estimate of corresponding signal properties.

Keywords

Linear Prediction Vocal Tract Linear Prediction Coefficient Speech Spectrum Real Speech 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Department of LinguisticsCalifornia State University FresnoFresnoUSA

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