Speaker Recognition.

Date of Submission

December 2005

Date of Award

Winter 12-12-2006

Institute Name (Publisher)

Indian Statistical Institute

Document Type

Master's Dissertation

Degree Name

Master of Technology

Subject Name

Computer Science


Computer Vision and Pattern Recognition Unit (CVPR-Kolkata)


Mitra, Mandar (CVPR-Kolkata; ISI)

Abstract (Summary of the Work)

We have concentrated on the speaker identification part of the speaker recognition problem. Here, we have made a study which involves the classifi- cation and identification of the speakers using the Gaussian mixture models (GMM) and the mel frequency cepstral coefficients (MFCC). Due to its re- ported superior performance, especially under adverse conditions, MFCC is becoming an increasingly popular choice as feature extraction front end to spoken language systems. The individual Gaussian components of a GMM are shown to represent some general speaker-dependent spectral shapes that are effective for modelling speaker identity. A complete experimental evaluation is conducted on two sets of data of 7 speakers and 21 speakers. The GMM attains 100% accuracy on the 7 speaker data and 97.3% on the 21 speaker data using clean speech utterances.


ProQuest Collection ID: http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:28843273

Control Number


Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.



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