Abstract
In this paper, we propose a new approach to robust speaker identification using KPCA (kernel principal component analysis). This approach uses ensembles of classifiers (speaker identifiers) to reduce KPCA computation. KPCA enhances the features for each classifier. To reduce the processing time and memory requirements, we select a subset of limited number of samples randomly which is used as estimation set for each KPCA basis. The experimental result shows that the proposed approach shows better accuracy than PCA and GKPCA (greedy KPCA).
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.
