Abstract
The speech production model 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 an impulse sequence and a noise sequence (voiced speech) is considered. Approximate maximum-likelihood (ML) estimation algorithms for the unvoiced case are well known. In this work, the expectation-maximization (EM) algorithm is used in order to obtain the ML estimator of the parameters for the voiced speech model. These parameters consist of the parameters of the impulse sequence (pitch parameters) and the parameters of the filter (autoregressive parameters). >
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
