dc.contributor.authorSong, Liyan.
dc.date.accessioned2012-04-25T04:21:08Z
dc.date.available2012-04-25T04:21:08Z
dc.date.copyright2012en_US
dc.date.issued2012
dc.identifier.urihttp://hdl.handle.net/10356/48504
dc.description.abstractThis report givens an overview of a Gaussian Mixture Model – Universal Background Model (GMM-UBM) system which focusing on speaker identification. In this report we will be focusing on the traditional FFT-based Mel-Frequency Cepstral Coefficients (MFCCs) method to extract feature from wav file and GMM-UBM to create speaker model. The detail information of MFCC and GMM-UBM will be explained in the report. The program is build based using GMM-UBM and MFCC, the likelihood ratio of the testing speech are the output of the program. The experiment is carry out to evaluate the effects on accuracy when different mixture and file of MFC are used.en_US
dc.format.extent41 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognitionen_US
dc.titleSpeaker recognition systemen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorChng Eng Siong (SCE)en_US
dc.contributor.schoolSchool of Computer Engineeringen_US
dc.description.degreeCOMPUTER SCIENCEen_US


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