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Speech Features Analysis and Biometric Person Identification in Multilingual Environment

V.K. Jain1 , N. Tripathi2

1 Electronics andTelecommunication, SSTC (SSGI),CSVTU, Bhilai, India.
2 Electronics and Telecommunication, SSTC (SSGI),CSVTU, Bhilai, India.

Correspondence should be addressed to: vinayrich_17@yahoo.co.in.

Section:Research Paper, Product Type: Journal
Vol.6 , Issue.1 , pp.7-11, Feb-2018

CrossRef-DOI:   https://doi.org/10.26438/ijsrnsc/v6i1.711

Online published on Feb 28, 2018

Copyright © V.K. Jain, N. Tripathi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Citation :
IEEE Style Citation: V.K. Jain, N. Tripathi, “Speech Features Analysis and Biometric Person Identification in Multilingual Environment”, International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.1, pp.7-11, 2018.

MLA Style Citation: V.K. Jain, N. Tripathi "Speech Features Analysis and Biometric Person Identification in Multilingual Environment." International Journal of Scientific Research in Network Security and Communication 6.1 (2018): 7-11.

APA Style Citation: V.K. Jain, N. Tripathi, (2018). Speech Features Analysis and Biometric Person Identification in Multilingual Environment. International Journal of Scientific Research in Network Security and Communication, 6(1), 7-11.

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Abstract :
Biometric person identification systems are the important in the environments where security must be needed. In this respect, a Biometric person identification systems has been designed which identify the person by determining the authenticity by their voice in multilingual environment. The speech samples are recorded in three Indian languages Hindi, Marathi and Rajasthani for multilingual environment. Pitch, formant frequencies, MFCC and GFCC feature are extracted from the speech signals. For training and testing, neural network using radial basis functions are used. In this experiment accuracy of Biometric person identification 96.52% has been achieved.

Key-Words / Index Term :
Biometric, Pitch, Formant, MFCC and GFCC

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