Lecture 13_winter_2012

Lecture 13_winter_2012 - Digital Speech Processing Lecture...

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1 Digital Speech Processing— Lecture 13 Linear Predictive Coding (LPC)- Introduction
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2 LPC Methods • LPC methods are the most widely used in speech coding, speech synthesis, speech recognition, speaker recognition and verification and for speech storage – LPC methods provide extremely accurate estimates of speech parameters, and does it extremely efficiently – basic idea of Linear Prediction: current speech sample can be closely approximated as a linear combination of past samples, i.e., 1 ( ) ( ) for some value of , 's αα = =− p kk k sn sn k p
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3 LPC Methods for periodic signals with period , it is obvious that () ( ) but that is not what LP is doing; it is estimating ( ) from the ( ) most recent values of ( ) by linearly predicting ≈− << p p p N sn sn N pp N s n its value for LP, the predictor coefficients (the 's) are determined (computed) by (over a finite interval) α k minimizing the sum of squared differences between the actual speech samples and the linearly predicted ones
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4 LPC Methods LP is based on speech production and synthesis models - speech can be modeled as the output of a linear, time-varying system, excited by either quasi-periodic pulses or noise; - assume that the model parameters remain constant over speech analysis interval LP provides a for estimating the parameters of the linear system (the com ± robust, reliable and accurate method bined vocal tract, glottal pulse, and radiation characteristic for voiced speech)
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5 LPC Methods LP methods have been used in control and information theory—called methods of system estimation and system identification used extensively in speech under group of names including 1. covariance method 2. autocorrelation method 3. lattice method 4. inverse filter formulation 5. spectral estimation formulation 6. maximum likelihood method 7. inner product method
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6 Basic Principles of LP 1 1 1 () = == p k k k Sz Hz GU z az 1 ( ) = =− + p k k sn asn k Gun • the time-varying digital filter represents the effects of the glottal pulse shape, the vocal tract IR, and radiation at the lips • the system is excited by an impulse train for voiced speech, or a random noise sequence for unvoiced speech • this ‘all-pole’ model is a natural representation for non-nasal voiced speech—but it also works reasonably well for nasals and unvoiced sounds
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7 LP Basic Equations 11 1 a order linear predictor is a system of the form () ( ) ( ) ( ) the prediction error, ( ), is of the form () () ( ) the pr αα α == = =− = = =−=− ∑∑ % % % th pp k kk p k k p Sz sn sn k Pz z en sn sn 1 1 ediction error is the output of a system with transfer function ( ) ( ) if the speech signal obeys the production model exactly, and if , ( ) ( )and ( ) = = •= ⇒= p k k k Ez Az z ak p Gun 1 is an inverse filter for ( ), i.e., ( ) = Hz
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8 LP Estimation Issues need to determine { } directly from speech such that they give good estimates of the time-varying spectrum need to estimate { } from short segments of speech need to minimize mean-squared pr α k k ediction error over
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This note was uploaded on 12/29/2011 for the course ECE 259 taught by Professor Rabiner,l during the Fall '08 term at UCSB.

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Lecture 13_winter_2012 - Digital Speech Processing Lecture...

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