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Study of statistical robust closed set speaker identification with feature and score-based fusion

Lookup NU author(s): Musab Al_kaltakchi, Dr Wai Lok Woo, Professor Satnam Dlay, Professor Jonathon Chambers

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Abstract

In this paper, the statistical combination of Power Normalization Cepstral Coefficient (PNCC) and Mel Frequency Cepstral Coefficient (MFCC) features in robust closed set speaker identification is studied. Feature normalization and warping together with late score-based fusion are also exploited to improve performance in the presence of channel and noise effects. In addition, combinations of score and feature-based approaches are considered with early and/or late fusion; these systems use different feature dimensions (16, 32). A 4th order G.712 type IIR filter is employed to represent handset degradation in the channel. Simulation studies based on the TIMIT database confirm the improvement in Speaker Identification Accuracy (SIA) through the combination of PNCC and MFCC features in the presence of handset and Additive White Gaussian Noise (AWGN) effects.


Publication metadata

Author(s): Al-Kaltakchi MTS, Woo WL, Dlay SS, Chambers JA

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: IEEE Statistical Signal Processing Workshop (SSP)

Year of Conference: 2016

Online publication date: 25/08/2016

Acceptance date: 01/01/1900

ISSN: 9781467378031

Publisher: Institute of Electrical and Electronics Engineers

URL: http://dx.doi.org/10.1109/SSP.2016.7551807

DOI: 10.1109/SSP.2016.7551807


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