Error Minimization For Language-Independent Speaker Identification System

Abstract

In this paper, error of intra-speaker speech of a language-independent speaker identification system is minimized. Well-known Mel Frequency Cepstral coefficients {MFCCs} have been used for feature extraction. The decision threshold is located at the point where the probabilities of both errors are equal. To make system realistic, we will make system model by using GMM (Gaussian Mixture Model). Using these models, we build speaker identifiers that are capable of identify a speaker regardless of the language of speaker.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Ajay Kumar Mishra
AU  - Amit Kaul
PY  - 2013/04
DA  - 2013/04
TI  - Error Minimization For Language-Independent Speaker Identification System
BT  - Proceedings of the Conference on Advances in Communication and Control Systems (CAC2S 2013)
PB  - Atlantis Press
SP  - 415
EP  - 419
SN  - 1951-6851
UR  - https://www.atlantis-press.com/article/6347
ID  - Mishra2013/04
ER  -