Speaker Recognition Using Adaptively Boosted Classifiers
Summary :
In this paper, a novel approach to speaker recognition is proposed. The approach makes use of adaptive boosting (AdaBoost) and classifiers such as Multilayer Perceptrons (MLP) and C4.5 Decision Trees for closed set, text-dependent speaker recognition. The performance of the systems is assessed using a subset of utterances drawn from the YOHO speaker verification corpus. Experiments show that significant improvement in accuracy can be achieved with the application of adaptive boosting techniques. Results also reveal that an accuracy of 98.8% for speaker identification may be achieved using the adaptively boosted C4.5 system.
- Publication
- IEICE TRANSACTIONS on Information Vol.E86-D No.3 pp.474-482
- Publication Date
- 2003/03/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section PAPER (Special Issue on Speech Information Processing)
- Category
- Speech and Speaker Recognition
Authors
Keyword
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Say-Wei FOO, Eng-Guan LIM, "Speaker Recognition Using Adaptively Boosted Classifiers" in IEICE TRANSACTIONS on Information,
vol. E86-D, no. 3, pp. 474-482, March 2003, doi: .
Abstract: In this paper, a novel approach to speaker recognition is proposed. The approach makes use of adaptive boosting (AdaBoost) and classifiers such as Multilayer Perceptrons (MLP) and C4.5 Decision Trees for closed set, text-dependent speaker recognition. The performance of the systems is assessed using a subset of utterances drawn from the YOHO speaker verification corpus. Experiments show that significant improvement in accuracy can be achieved with the application of adaptive boosting techniques. Results also reveal that an accuracy of 98.8% for speaker identification may be achieved using the adaptively boosted C4.5 system.
URL: https://global.ieice.org/en_transactions/information/10.1587/e86-d_3_474/_p
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@ARTICLE{e86-d_3_474,
author={Say-Wei FOO, Eng-Guan LIM, },
journal={IEICE TRANSACTIONS on Information},
title={Speaker Recognition Using Adaptively Boosted Classifiers},
year={2003},
volume={E86-D},
number={3},
pages={474-482},
abstract={In this paper, a novel approach to speaker recognition is proposed. The approach makes use of adaptive boosting (AdaBoost) and classifiers such as Multilayer Perceptrons (MLP) and C4.5 Decision Trees for closed set, text-dependent speaker recognition. The performance of the systems is assessed using a subset of utterances drawn from the YOHO speaker verification corpus. Experiments show that significant improvement in accuracy can be achieved with the application of adaptive boosting techniques. Results also reveal that an accuracy of 98.8% for speaker identification may be achieved using the adaptively boosted C4.5 system.},
keywords={},
doi={},
ISSN={},
month={March},}
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TY - JOUR
TI - Speaker Recognition Using Adaptively Boosted Classifiers
T2 - IEICE TRANSACTIONS on Information
SP - 474
EP - 482
AU - Say-Wei FOO
AU - Eng-Guan LIM
PY - 2003
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E86-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2003
AB - In this paper, a novel approach to speaker recognition is proposed. The approach makes use of adaptive boosting (AdaBoost) and classifiers such as Multilayer Perceptrons (MLP) and C4.5 Decision Trees for closed set, text-dependent speaker recognition. The performance of the systems is assessed using a subset of utterances drawn from the YOHO speaker verification corpus. Experiments show that significant improvement in accuracy can be achieved with the application of adaptive boosting techniques. Results also reveal that an accuracy of 98.8% for speaker identification may be achieved using the adaptively boosted C4.5 system.
ER -
English
