Experiments on the MFCC application in speaker recognition using Matlab

Kritagya Bhattarai; P. W.C. Prasad; Abeer Alsadoon; L. Pham; Amr Elchouemi

doi:10.1109/ICIST.2017.7926796

Experiments on the MFCC application in speaker recognition using Matlab

Kritagya Bhattarai, P. W.C. Prasad, Abeer Alsadoon, L. Pham, Amr Elchouemi

Research output: Book chapter/Published conference paper › Conference paper › peer-review

19 Citations (Scopus)

Abstract

Speaker recognition is a very important research area where speech synthesis, and speech noise reduction are some of the major research areas. Speaker recognition is a new challenge for technologies. Many algorithms have been suggested and developed for feature extraction. This paper presents a feature extraction technique for speaker recognition using Mel Frequency Cepstral Coefficients (MFCC). Further, this paper evaluates experiments conducted along each step of the MFCC process. Finally, the paper compares hamming window and rectangular window technique based on the number of filters for accurate and efficient result in a Matlab environment. The result indicates that using a 32 filter with hamming window has more accuracy and efficiency compared to other windowing techniques and number of filters.

Original language	English
Title of host publication	Seventh International Conference on Information Science and Technology
Subtitle of host publication	ICIST 2017 - Proceedings
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	32-37
Number of pages	6
ISBN (Electronic)	9781509054015
ISBN (Print)	97810509054022 (Print on demand)
DOIs	https://doi.org/10.1109/ICIST.2017.7926796
Publication status	E-pub ahead of print - 15 May 2017
Event	7th International Conference on Information Science and Technology, ICIST 2017 - Da Nang, Viet Nam Duration: 16 Apr 2017 → 19 Apr 2017 https://conference.cs.cityu.edu.hk/icist/ICIST2017_CFP.pdf

Conference

Conference	7th International Conference on Information Science and Technology, ICIST 2017
Country/Territory	Viet Nam
City	Da Nang
Period	16/04/17 → 19/04/17
Internet address	https://conference.cs.cityu.edu.hk/icist/ICIST2017_CFP.pdf

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10.1109/ICIST.2017.7926796

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Bhattarai, K., Prasad, P. W. C., Alsadoon, A., Pham, L., & Elchouemi, A. (2017). Experiments on the MFCC application in speaker recognition using Matlab. In Seventh International Conference on Information Science and Technology: ICIST 2017 - Proceedings (pp. 32-37). Article 7926796 IEEE, Institute of Electrical and Electronics Engineers. Advance online publication. https://doi.org/10.1109/ICIST.2017.7926796

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title = "Experiments on the MFCC application in speaker recognition using Matlab",

abstract = "Speaker recognition is a very important research area where speech synthesis, and speech noise reduction are some of the major research areas. Speaker recognition is a new challenge for technologies. Many algorithms have been suggested and developed for feature extraction. This paper presents a feature extraction technique for speaker recognition using Mel Frequency Cepstral Coefficients (MFCC). Further, this paper evaluates experiments conducted along each step of the MFCC process. Finally, the paper compares hamming window and rectangular window technique based on the number of filters for accurate and efficient result in a Matlab environment. The result indicates that using a 32 filter with hamming window has more accuracy and efficiency compared to other windowing techniques and number of filters.",

keywords = "Feature matching, Mel Frequency Cepstral Coefficient (MFCC), Speaker recognition, Vector quantization",

author = "Kritagya Bhattarai and Prasad, {P. W.C.} and Abeer Alsadoon and L. Pham and Amr Elchouemi",

note = "Includes bibliographical references.; 7th International Conference on Information Science and Technology, ICIST 2017 ; Conference date: 16-04-2017 Through 19-04-2017",

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Bhattarai, K, Prasad, PWC, Alsadoon, A, Pham, L & Elchouemi, A 2017, Experiments on the MFCC application in speaker recognition using Matlab. in Seventh International Conference on Information Science and Technology: ICIST 2017 - Proceedings., 7926796, IEEE, Institute of Electrical and Electronics Engineers, pp. 32-37, 7th International Conference on Information Science and Technology, ICIST 2017, Da Nang, Viet Nam, 16/04/17. https://doi.org/10.1109/ICIST.2017.7926796

Experiments on the MFCC application in speaker recognition using Matlab. / Bhattarai, Kritagya; Prasad, P. W.C.; Alsadoon, Abeer et al.
Seventh International Conference on Information Science and Technology: ICIST 2017 - Proceedings. IEEE, Institute of Electrical and Electronics Engineers, 2017. p. 32-37 7926796.

Research output: Book chapter/Published conference paper › Conference paper › peer-review

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T1 - Experiments on the MFCC application in speaker recognition using Matlab

AU - Bhattarai, Kritagya

AU - Prasad, P. W.C.

AU - Alsadoon, Abeer

AU - Pham, L.

AU - Elchouemi, Amr

N1 - Includes bibliographical references.

PY - 2017/5/15

Y1 - 2017/5/15

N2 - Speaker recognition is a very important research area where speech synthesis, and speech noise reduction are some of the major research areas. Speaker recognition is a new challenge for technologies. Many algorithms have been suggested and developed for feature extraction. This paper presents a feature extraction technique for speaker recognition using Mel Frequency Cepstral Coefficients (MFCC). Further, this paper evaluates experiments conducted along each step of the MFCC process. Finally, the paper compares hamming window and rectangular window technique based on the number of filters for accurate and efficient result in a Matlab environment. The result indicates that using a 32 filter with hamming window has more accuracy and efficiency compared to other windowing techniques and number of filters.

AB - Speaker recognition is a very important research area where speech synthesis, and speech noise reduction are some of the major research areas. Speaker recognition is a new challenge for technologies. Many algorithms have been suggested and developed for feature extraction. This paper presents a feature extraction technique for speaker recognition using Mel Frequency Cepstral Coefficients (MFCC). Further, this paper evaluates experiments conducted along each step of the MFCC process. Finally, the paper compares hamming window and rectangular window technique based on the number of filters for accurate and efficient result in a Matlab environment. The result indicates that using a 32 filter with hamming window has more accuracy and efficiency compared to other windowing techniques and number of filters.

KW - Feature matching

KW - Mel Frequency Cepstral Coefficient (MFCC)

KW - Speaker recognition

KW - Vector quantization

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ER -

Experiments on the MFCC application in speaker recognition using Matlab

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