Joint modeling and maximum-likelihood estimation of pitch and linear prediction coefficient parameters.
The well-known speech production model is considered, where the speech signal is modeled as the output of an all-pole filter driven either by some white noise sequence (unvoiced speech) or by the sum of a periodic excitation and a noise sequence (voiced speech). Approximate maximum-likelihood (ML) estimation algorithms for the unvoiced case are well known. The ML estimator of the parameters is obtained for the voiced speech model. These parameters consist of the parameters of the periodic excitation (pitch parameters) and the parameters of the filter [linear prediction coefficient (LPC) parameters]. The results of the application of the algorithm on simulated and on real speech data are presented.
Burshtein
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Tel-Aviv University, Department of Electrical Engineering-Systems, Israel.
D000465:Algorithms; D003143:Communication Devices for People with Disabilities; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*; D013018:Sound Spectrography / Q000295:instrumentation*; D013060:Speech