Abstract
The construction of dynamic (delta) features of speech, which has been in the past confined to the pre-processing domain in hidden Markov modelling (HMM), is generalized and formulated as an integrated speech modeling problem. This generalization allows to utilize state-dependent weights to transform static speech features into dynamic ones. The author describes a rigorous theoretical framework that naturally incorporates the generalized dynamic-parameter technique, and presents a maximum-likelihood based algorithm for integrated optimization of the conventional HMM parameters and of the time-varying weighting functions that define the dynamic features of speech. >
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.
