Abstract
The aim is to make machine inevitably to identify a person or feasibly to substantiate a person's claimed identity through their speech. This paper deals with the Speaker Identification System which uses MFCC in training phase for extracting features. The system is being analyzed with and without applying Voice Conversion Attack using GMM which is a part of spoofing attack. In testing phase, the extracted features are given to the speaker design and the decisions are obtained by comparing the extracted parameters which are stored in database with the speaker's voice parameter. The parameter being analyzed is Speaker Identification Characterization(SIC).
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.
