Combining evidence from spectral and source-like features for person recognition from humming

Hemant A. Patil; Maulik C. Madhavi; Keshab K. Parhi

Combining evidence from spectral and source-like features for person recognition from humming

Hemant A. Patil, Maulik C. Madhavi, Keshab K. Parhi

Electrical and Computer Engineering

Research output: Contribution to journal › Conference article

8 Scopus citations

Abstract

In this paper, hum of a person is used in voice biometric system. In addition, recently proposed feature set, i.e., Variable length Teager Energy Based Mel Frequency Cepstral Coefficients (VTMFCC), is found to capture perceptually meaningful source-like information from hum signal. For person recognition, MFCC gives EER of 13.14% and %ID of 64.96%. A reduction in equal error rate (EER) by 0.2% and improvement in identification rate by 7.3 % is achieved when a score-level fusion system is employed by combining evidence from MFCC (system) and VTMFCC (source-like features) than MFCC alone. Results are reported for various feature dimensions and population sizes.

Original language	English (US)
Pages (from-to)	369-372
Number of pages	4
Journal	Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
State	Published - Dec 1 2011
Event	12th Annual Conference of the International Speech Communication Association, INTERSPEECH 2011 - Florence, Italy Duration: Aug 27 2011 → Aug 31 2011

Keywords

Fusion of Source-System features
Humming
Polynomial classifier
VTEO
VTMFCC

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Patil, H. A., Madhavi, M. C., & Parhi, K. K. (2011). Combining evidence from spectral and source-like features for person recognition from humming. Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 369-372.

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title = "Combining evidence from spectral and source-like features for person recognition from humming",

abstract = "In this paper, hum of a person is used in voice biometric system. In addition, recently proposed feature set, i.e., Variable length Teager Energy Based Mel Frequency Cepstral Coefficients (VTMFCC), is found to capture perceptually meaningful source-like information from hum signal. For person recognition, MFCC gives EER of 13.14% and %ID of 64.96%. A reduction in equal error rate (EER) by 0.2% and improvement in identification rate by 7.3 % is achieved when a score-level fusion system is employed by combining evidence from MFCC (system) and VTMFCC (source-like features) than MFCC alone. Results are reported for various feature dimensions and population sizes.",

keywords = "Fusion of Source-System features, Humming, Polynomial classifier, VTEO, VTMFCC",

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year = "2011",

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journal = "Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH",

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AU - Patil, Hemant A.

AU - Madhavi, Maulik C.

AU - Parhi, Keshab K.

PY - 2011/12/1

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N2 - In this paper, hum of a person is used in voice biometric system. In addition, recently proposed feature set, i.e., Variable length Teager Energy Based Mel Frequency Cepstral Coefficients (VTMFCC), is found to capture perceptually meaningful source-like information from hum signal. For person recognition, MFCC gives EER of 13.14% and %ID of 64.96%. A reduction in equal error rate (EER) by 0.2% and improvement in identification rate by 7.3 % is achieved when a score-level fusion system is employed by combining evidence from MFCC (system) and VTMFCC (source-like features) than MFCC alone. Results are reported for various feature dimensions and population sizes.

AB - In this paper, hum of a person is used in voice biometric system. In addition, recently proposed feature set, i.e., Variable length Teager Energy Based Mel Frequency Cepstral Coefficients (VTMFCC), is found to capture perceptually meaningful source-like information from hum signal. For person recognition, MFCC gives EER of 13.14% and %ID of 64.96%. A reduction in equal error rate (EER) by 0.2% and improvement in identification rate by 7.3 % is achieved when a score-level fusion system is employed by combining evidence from MFCC (system) and VTMFCC (source-like features) than MFCC alone. Results are reported for various feature dimensions and population sizes.

KW - Fusion of Source-System features

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KW - Polynomial classifier

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