Adapted Gaussian mixture model in likelihood ratio based forensic voice comparison using long term fundamental frequency

Carolin Buncle Diesner; Shunichi Ishihara

Adapted Gaussian mixture model in likelihood ratio based forensic voice comparison using long term fundamental frequency

Carolin Buncle Diesner, Shunichi Ishihara

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this paper, the Gaussian Mixture Model Universal Background Model (GMM-UBM) is applied to onedimensional speech data, namely the distribution of long term fundamental frequency (LTF0) in likelihood ratio based forensic voice comparison. A series of experiments were conducted using varying numbers of Gaussians, differing adaptation rates to a UBM, and different lengths of speech samples. The results of the GMM-UBM procedure are compared to two previously proposed procedures for LTF0. All three procedures exhibited unique characteristics in their performances. Thus, there was no consistency in performance in that no one procedure constantly outperformed the others. Index Terms: forensic voice comparison, likelihood ratio, GMM-UBM, long-term F0 distribution

Original language	English
Title of host publication	Proceedings of the Sixteenth Australasian International Conference on Speech Science and Technology
Editors	Christopher Carignan and Michael D. Tyler
Place of Publication	Parramatta, Australia
Publisher	The Australasian Speech Science and Technology Association, Inc.
Pages	141-144pp
Edition	Peer Reviewed
ISBN (Print)	2207-1296
Publication status	Published - 2016
Event	16th Australasian International Conference on Speech Science and Technology - Parramatta, Australia Duration: 6 Dec 2016 → 9 Dec 2016 https://webarchive.nla.gov.au/awa/20180320002425/http://sst2016.westernsydney.edu.au/ https://webarchive.nla.gov.au/awa/20240426142921/https://assta.org/wp-content/uploads/2019/09/SST2016_Proceedings.pdf

Conference

Conference	16th Australasian International Conference on Speech Science and Technology
Abbreviated title	SST2016
Country/Territory	Australia
City	Parramatta
Period	6/12/16 → 9/12/16
Other	The Australasian Speech Science and Technology Association and Western Sydney University are pleased to host the 16th Australasian International Conference on Speech Science and Technology (SST2016). This conference will mark the 30-year anniversary of the first SST conference held in Canberra in 1986.
Internet address	https://webarchive.nla.gov.au/awa/20180320002425/http://sst2016.westernsydney.edu.au/ https://webarchive.nla.gov.au/awa/20240426142921/https://assta.org/wp-content/uploads/2019/09/SST2016_Proceedings.pdf

Cite this

Buncle Diesner, C., & Ishihara, S. (2016). Adapted Gaussian mixture model in likelihood ratio based forensic voice comparison using long term fundamental frequency. In C. C. A. M. D. T. (Ed.), Proceedings of the Sixteenth Australasian International Conference on Speech Science and Technology (Peer Reviewed ed., pp. 141-144pp). The Australasian Speech Science and Technology Association, Inc..

Buncle Diesner, Carolin ; Ishihara, Shunichi. / Adapted Gaussian mixture model in likelihood ratio based forensic voice comparison using long term fundamental frequency. Proceedings of the Sixteenth Australasian International Conference on Speech Science and Technology. editor / Christopher Carignan and Michael D. Tyler. Peer Reviewed. ed. Parramatta, Australia : The Australasian Speech Science and Technology Association, Inc., 2016. pp. 141-144pp

@inproceedings{12a24b2542284b0d8ba95c57dfd679a8,

title = "Adapted Gaussian mixture model in likelihood ratio based forensic voice comparison using long term fundamental frequency",

abstract = "In this paper, the Gaussian Mixture Model Universal Background Model (GMM-UBM) is applied to onedimensional speech data, namely the distribution of long term fundamental frequency (LTF0) in likelihood ratio based forensic voice comparison. A series of experiments were conducted using varying numbers of Gaussians, differing adaptation rates to a UBM, and different lengths of speech samples. The results of the GMM-UBM procedure are compared to two previously proposed procedures for LTF0. All three procedures exhibited unique characteristics in their performances. Thus, there was no consistency in performance in that no one procedure constantly outperformed the others. Index Terms: forensic voice comparison, likelihood ratio, GMM-UBM, long-term F0 distribution",

author = "{Buncle Diesner}, Carolin and Shunichi Ishihara",

year = "2016",

language = "English",

isbn = "2207-1296",

pages = "141--144pp",

editor = "{Christopher Carignan and Michael D. Tyler}",

booktitle = "Proceedings of the Sixteenth Australasian International Conference on Speech Science and Technology",

publisher = "The Australasian Speech Science and Technology Association, Inc.",

edition = "Peer Reviewed",

note = "16th Australasian International Conference on Speech Science and Technology, SST2016 ; Conference date: 06-12-2016 Through 09-12-2016",

url = "https://webarchive.nla.gov.au/awa/20180320002425/http://sst2016.westernsydney.edu.au/, https://webarchive.nla.gov.au/awa/20240426142921/https://assta.org/wp-content/uploads/2019/09/SST2016_Proceedings.pdf",

}

Buncle Diesner, C & Ishihara, S 2016, Adapted Gaussian mixture model in likelihood ratio based forensic voice comparison using long term fundamental frequency. in CCAMDT (ed.), Proceedings of the Sixteenth Australasian International Conference on Speech Science and Technology. Peer Reviewed edn, The Australasian Speech Science and Technology Association, Inc., Parramatta, Australia, pp. 141-144pp, 16th Australasian International Conference on Speech Science and Technology, Parramatta, New South Wales, Australia, 6/12/16.

Adapted Gaussian mixture model in likelihood ratio based forensic voice comparison using long term fundamental frequency. / Buncle Diesner, Carolin; Ishihara, Shunichi.
Proceedings of the Sixteenth Australasian International Conference on Speech Science and Technology. ed. / Christopher Carignan and Michael D. Tyler. Peer Reviewed. ed. Parramatta, Australia: The Australasian Speech Science and Technology Association, Inc., 2016. p. 141-144pp.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Adapted Gaussian mixture model in likelihood ratio based forensic voice comparison using long term fundamental frequency

AU - Buncle Diesner, Carolin

AU - Ishihara, Shunichi

PY - 2016

Y1 - 2016

N2 - In this paper, the Gaussian Mixture Model Universal Background Model (GMM-UBM) is applied to onedimensional speech data, namely the distribution of long term fundamental frequency (LTF0) in likelihood ratio based forensic voice comparison. A series of experiments were conducted using varying numbers of Gaussians, differing adaptation rates to a UBM, and different lengths of speech samples. The results of the GMM-UBM procedure are compared to two previously proposed procedures for LTF0. All three procedures exhibited unique characteristics in their performances. Thus, there was no consistency in performance in that no one procedure constantly outperformed the others. Index Terms: forensic voice comparison, likelihood ratio, GMM-UBM, long-term F0 distribution

AB - In this paper, the Gaussian Mixture Model Universal Background Model (GMM-UBM) is applied to onedimensional speech data, namely the distribution of long term fundamental frequency (LTF0) in likelihood ratio based forensic voice comparison. A series of experiments were conducted using varying numbers of Gaussians, differing adaptation rates to a UBM, and different lengths of speech samples. The results of the GMM-UBM procedure are compared to two previously proposed procedures for LTF0. All three procedures exhibited unique characteristics in their performances. Thus, there was no consistency in performance in that no one procedure constantly outperformed the others. Index Terms: forensic voice comparison, likelihood ratio, GMM-UBM, long-term F0 distribution

M3 - Conference contribution

SN - 2207-1296

SP - 141

EP - 144

BT - Proceedings of the Sixteenth Australasian International Conference on Speech Science and Technology

A2 - null, Christopher Carignan and Michael D. Tyler

PB - The Australasian Speech Science and Technology Association, Inc.

CY - Parramatta, Australia

T2 - 16th Australasian International Conference on Speech Science and Technology

Y2 - 6 December 2016 through 9 December 2016

ER -

Buncle Diesner C, Ishihara S. Adapted Gaussian mixture model in likelihood ratio based forensic voice comparison using long term fundamental frequency. In CCAMDT, editor, Proceedings of the Sixteenth Australasian International Conference on Speech Science and Technology. Peer Reviewed ed. Parramatta, Australia: The Australasian Speech Science and Technology Association, Inc. 2016. p. 141-144pp

Adapted Gaussian mixture model in likelihood ratio based forensic voice comparison using long term fundamental frequency

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