VOICE RECOGNITION SECURITY SYSTEM USING MEL-FREQUENCY CEPSTRUM COEFFICIENTS

Mahalakshmi P; Muruganandam M; Sharmila A

doi:10.22159/ajpcr.2016.v9s3.13633

Authors

Mahalakshmi P VIT University, Vellore -632 014, India.
Muruganandam M
Sharmila A

DOI:

https://doi.org/10.22159/ajpcr.2016.v9s3.13633

Abstract

ABSTRACT
Objective: Voice Recognition is a fascinating field spanning several areas of computer science and mathematics. Reliable speaker recognition is a hard
problem, requiring a combination of many techniques; however modern methods have been able to achieve an impressive degree of accuracy. The
objective of this work is to examine various speech and speaker recognition techniques and to apply them to build a simple voice recognition system.
Method: The project is implemented on software which uses different techniques such as Mel frequency Cepstrum Coefficient (MFCC), Vector
Quantization (VQ) which are implemented using MATLAB.
Results: MFCC is used to extract the characteristics from the input speech signal with respect to a particular word uttered by a particular speaker. VQ
codebook is generated by clustering the training feature vectors of each speaker and then stored in the speaker database.
Conclusion: Verification of the speaker is carried out using Euclidian Distance. For voice recognition we implement the MFCC approach using software
platform MatlabR2013b.
Keywords: Mel-frequency cepstrum coefficient, Vector quantization, Voice recognition, Hidden Markov model, Euclidean distance.

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Author Biography

Mahalakshmi P, VIT University, Vellore -632 014, India.

School of Electrical Engineering

References

Gong Y. Speech recognition in noisy environments: A Survey. Speech Commun 1995;9(3):261-91.

Muda L, Begam M, Elamvazuthi I. Voice recognition algorithm using Mel frequency Cepstral coefficient and dynamic time wraping techniques. J Comput 2010 2(3):138-43.

Kekre, HB. Athawala, AA, Sharma, GJ. Speech Recognition using Vector Quantization. International Conference & Workshop on Emerging Trends in Technology, 2011. p. 16-24.

Swamy S, Ramakrishnan KV. A review on speech recognition with hidden Markov model. Int J Comput Appl 2013;3(4):16-24.

Rabiner L. A tutorial on hidden Markov models and selected application in speech recognition. Proc IEEE 1989;77(2):257-86.

Bhupinder S, Neha K, Puneet K. A review on speech recognition with hidden Markov model. Int J Comput Appl 2012;2(3):16-24.

Hinton G, Li D, Yu D, Dahl GE. Deep neural networks for acoustic modeling in speech recognition. Signal Process Mag IEEE 2012;29(4):82-97.

Trentin E, Gori M. A Survey of hybrid ANN/HMM models for automatic speech recognition. Neuro Comput 2001;37(1-4):91-126.

Ghosh D, Debnath D, Bose S. A Comparitive study of performance of FPGA based Mel filter bank and Bark Filter Bank. Int J Artif Intell Appl 2012;3(3):37.

Alan V, Ronald SW, John RB. Discrete Signal Processing. New Jersey: Prentice Hall; 1999.

Bexhetti B, Ricotti A. Speech Recognition. New York: Wiley; 1999.