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Acoustic and facial features for speaker recognition / Roach M.J.; Brand J.D.; Mason J.S.D.

Proceedings 15th International Conference on Pattern Recognition. ICPR-2000

Swansea University Author: Roach, Matthew

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DOI (Published version): 10.1109/ICPR.2000.903534

Abstract

This paper gives an insight into biometrics used for speaker recognition. Three different biometrics are presented, based on: acoustic, geometric lip, and holistic facial features. Experiments are carried out using a corpus of the DAVID audio-visual database. Recognition accuracy is found to be simi...

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Published in: Proceedings 15th International Conference on Pattern Recognition. ICPR-2000
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URI: https://cronfa.swan.ac.uk/Record/cronfa39141
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spelling 2018-03-21T20:19:50Z v2 39141 2018-03-21 Acoustic and facial features for speaker recognition Matthew Roach Matthew Roach true 0000-0002-1486-5537 false 9722c301d5bbdc96e967cdc629290fec 2bf0d8407fd3a4dd733f4647674f27cf Bhwk3TwcVU8jZH9JHyKzUggr5y2nBRz3haj4DmVVDsQ= 2018-03-21 CSCI This paper gives an insight into biometrics used for speaker recognition. Three different biometrics are presented, based on: acoustic, geometric lip, and holistic facial features. Experiments are carried out using a corpus of the DAVID audio-visual database. Recognition accuracy is found to be similar in the 2 domains. The geometric visual feature is based on a method of signature coding of the contour of the lips and the holistic feature is based on a mean dynamic signature, a method of capturing the motions of the face during a spoken utterance. Physical biometrics (static measurements) demand only small model sizes, perhaps just a single template, and therefore require less training data. Conversely behavioral biometrics contain more variation and demand more training data Other Proceedings 15th International Conference on Pattern Recognition. ICPR-2000 0 0 0 0001-01-01 10.1109/ICPR.2000.903534 College of Science College CSCI CSCI 2018-03-21T20:19:50Z 2018-03-21T20:19:50Z College of Science Computer Science Roach M.J. 0 Brand J.D. 0 Mason J.S.D. 0
title Acoustic and facial features for speaker recognition
spellingShingle Acoustic and facial features for speaker recognition
Roach, Matthew
title_short Acoustic and facial features for speaker recognition
title_full Acoustic and facial features for speaker recognition
title_fullStr Acoustic and facial features for speaker recognition
title_full_unstemmed Acoustic and facial features for speaker recognition
title_sort Acoustic and facial features for speaker recognition
author_id_str_mv 9722c301d5bbdc96e967cdc629290fec
author_id_fullname_str_mv 9722c301d5bbdc96e967cdc629290fec_***_Roach, Matthew
author Roach, Matthew
author2 Roach M.J.
Brand J.D.
Mason J.S.D.
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container_title Proceedings 15th International Conference on Pattern Recognition. ICPR-2000
institution Swansea University
doi_str_mv 10.1109/ICPR.2000.903534
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hierarchy_top_title College of Science
hierarchy_parent_id collegeofscience
hierarchy_parent_title College of Science
department_str Computer Science{{{_:::_}}}College of Science{{{_:::_}}}Computer Science
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description This paper gives an insight into biometrics used for speaker recognition. Three different biometrics are presented, based on: acoustic, geometric lip, and holistic facial features. Experiments are carried out using a corpus of the DAVID audio-visual database. Recognition accuracy is found to be similar in the 2 domains. The geometric visual feature is based on a method of signature coding of the contour of the lips and the holistic feature is based on a mean dynamic signature, a method of capturing the motions of the face during a spoken utterance. Physical biometrics (static measurements) demand only small model sizes, perhaps just a single template, and therefore require less training data. Conversely behavioral biometrics contain more variation and demand more training data
published_date 0001-01-01T21:58:22Z
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score 10.837508