Enhanced forensic speaker verification using multi-run ICA in the presence of environmental noise and reverberation conditions

Al-Ali, AKH; Senadji, B; Naik, GR

Enhanced forensic speaker verification using multi-run ICA in the presence of environmental noise and reverberation conditions

Al-Ali, AKH Senadji, B Naik, GR

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2017 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017, 2017, pp. 174 - 179
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Al-Ali, AKH	en_US
dc.contributor.author	Senadji, B	en_US
dc.contributor.author	Naik, GR https://orcid.org/0000-0003-1790-9838	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	Proceedings of the 2017 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017, 2017, pp. 174 - 179	en_US
dc.identifier.isbn	9781509055593	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127312
dc.description.abstract	© 2017 IEEE. The performance of forensic speaker verification degrades severely in the presence of high levels of environmental noise and reverberation conditions. Multiple channel speech enhancement algorithms are a possible solution to reduce the effect of environmental noise from the noisy speech signals. Although multiple speech enhancement algorithms such as multi-run independent component analysis (ICA) were used in previous studies to improve the performance of recognition in biosignal applications, the effectiveness of multi-run ICA algorithm to improve the performance of noisy forensic speaker verification under reverberation conditions has not been investigated yet. In this paper, the multi-run ICA algorithm is used to enhance the noisy speech signals by choosing the highest signal to interference ratio (SIR) of the mixing matrix from different mixing matrices generated by iterating the fast ICA algorithm for several times. Wavelet-based mel frequency cepstral coefficients (MFCCs) feature warping approach is applied to the enhanced speech signals to extract the robust features to environmental noise and reverberation conditions. The state-of-The-Art intermediate vector (i-vector) and probabilistic linear discriminant analysis (PLDA) are used as a classifier in our approach. Experimental results show that forensic speaker verification based on the multi-run ICA algorithm achieves significant improvements in equal error rate (EER) of 60.88%, 51.84%, 66.15% over the baseline noisy speaker verification when enrolment speech signals reverberated at 0.15 sec and the test speech signals were mixed with STREET, CAR and HOME noises respectively at-10 dB signal to noise ratio (SNR).	en_US
dc.relation.ispartof	Proceedings of the 2017 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017	en_US
dc.relation.isbasedon	10.1109/ICSIPA.2017.8120601	en_US
dc.title	Enhanced forensic speaker verification using multi-run ICA in the presence of environmental noise and reverberation conditions	en_US
dc.type	Conference Proceeding
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2017 IEEE. The performance of forensic speaker verification degrades severely in the presence of high levels of environmental noise and reverberation conditions. Multiple channel speech enhancement algorithms are a possible solution to reduce the effect of environmental noise from the noisy speech signals. Although multiple speech enhancement algorithms such as multi-run independent component analysis (ICA) were used in previous studies to improve the performance of recognition in biosignal applications, the effectiveness of multi-run ICA algorithm to improve the performance of noisy forensic speaker verification under reverberation conditions has not been investigated yet. In this paper, the multi-run ICA algorithm is used to enhance the noisy speech signals by choosing the highest signal to interference ratio (SIR) of the mixing matrix from different mixing matrices generated by iterating the fast ICA algorithm for several times. Wavelet-based mel frequency cepstral coefficients (MFCCs) feature warping approach is applied to the enhanced speech signals to extract the robust features to environmental noise and reverberation conditions. The state-of-The-Art intermediate vector (i-vector) and probabilistic linear discriminant analysis (PLDA) are used as a classifier in our approach. Experimental results show that forensic speaker verification based on the multi-run ICA algorithm achieves significant improvements in equal error rate (EER) of 60.88%, 51.84%, 66.15% over the baseline noisy speaker verification when enrolment speech signals reverberated at 0.15 sec and the test speech signals were mixed with STREET, CAR and HOME noises respectively at-10 dB signal to noise ratio (SNR).

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