Robust speaker identification using ensembles of kernel principal component analysis

Il Ho Yang; Min Seok Kim; Byung Min So; Myung Jae Kim; Ha Jin Yu

doi:10.1007/978-3-642-28942-2_7

Robust speaker identification using ensembles of kernel principal component analysis

Il Ho Yang, Min Seok Kim, Byung Min So, Myung Jae Kim, Ha Jin Yu

Department of Computer Science and Engineering

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

2 Scopus citations

Abstract

In this paper, we propose a new approach to robust speaker identification using KPCA (kernel principal component analysis). This approach uses ensembles of classifiers (speaker identifiers) to reduce KPCA computation. KPCA enhances the features for each classifier. To reduce the processing time and memory requirements, we select a subset of limited number of samples randomly which is used as estimation set for each KPCA basis. The experimental result shows that the proposed approach shows better accuracy than PCA and GKPCA (greedy KPCA).

Original language	English
Title of host publication	Hybrid Artificial Intelligent Systems - 7th International Conference, HAIS 2012, Proceedings
Pages	71-78
Number of pages	8
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-28942-2_7
State	Published - 2012
Event	7th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2012 - Salamanca, Spain Duration: 28 Mar 2012 → 30 Mar 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	7208 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	7th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2012
Country/Territory	Spain
City	Salamanca
Period	28/03/12 → 30/03/12

Keywords

classifier ensemble
greedy kernel PCA
speaker identification

Access to Document

10.1007/978-3-642-28942-2_7

Cite this

Yang, I. H., Kim, M. S., So, B. M., Kim, M. J., & Yu, H. J. (2012). Robust speaker identification using ensembles of kernel principal component analysis. In Hybrid Artificial Intelligent Systems - 7th International Conference, HAIS 2012, Proceedings (PART 1 ed., pp. 71-78). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7208 LNAI, No. PART 1). https://doi.org/10.1007/978-3-642-28942-2_7

Yang, Il Ho ; Kim, Min Seok ; So, Byung Min et al. / Robust speaker identification using ensembles of kernel principal component analysis. Hybrid Artificial Intelligent Systems - 7th International Conference, HAIS 2012, Proceedings. PART 1. ed. 2012. pp. 71-78 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

@inproceedings{67ccca7c792f4dae9337b94b0495dc17,

title = "Robust speaker identification using ensembles of kernel principal component analysis",

abstract = "In this paper, we propose a new approach to robust speaker identification using KPCA (kernel principal component analysis). This approach uses ensembles of classifiers (speaker identifiers) to reduce KPCA computation. KPCA enhances the features for each classifier. To reduce the processing time and memory requirements, we select a subset of limited number of samples randomly which is used as estimation set for each KPCA basis. The experimental result shows that the proposed approach shows better accuracy than PCA and GKPCA (greedy KPCA).",

keywords = "classifier ensemble, greedy kernel PCA, speaker identification",

author = "Yang, {Il Ho} and Kim, {Min Seok} and So, {Byung Min} and Kim, {Myung Jae} and Yu, {Ha Jin}",

year = "2012",

doi = "10.1007/978-3-642-28942-2_7",

language = "English",

isbn = "9783642289415",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

number = "PART 1",

pages = "71--78",

booktitle = "Hybrid Artificial Intelligent Systems - 7th International Conference, HAIS 2012, Proceedings",

edition = "PART 1",

note = "7th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2012 ; Conference date: 28-03-2012 Through 30-03-2012",

}

Yang, IH, Kim, MS, So, BM, Kim, MJ & Yu, HJ 2012, Robust speaker identification using ensembles of kernel principal component analysis. in Hybrid Artificial Intelligent Systems - 7th International Conference, HAIS 2012, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 7208 LNAI, pp. 71-78, 7th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2012, Salamanca, Spain, 28/03/12. https://doi.org/10.1007/978-3-642-28942-2_7

Robust speaker identification using ensembles of kernel principal component analysis. / Yang, Il Ho; Kim, Min Seok; So, Byung Min et al.
Hybrid Artificial Intelligent Systems - 7th International Conference, HAIS 2012, Proceedings. PART 1. ed. 2012. p. 71-78 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7208 LNAI, No. PART 1).

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

TY - GEN

T1 - Robust speaker identification using ensembles of kernel principal component analysis

AU - Yang, Il Ho

AU - Kim, Min Seok

AU - So, Byung Min

AU - Kim, Myung Jae

AU - Yu, Ha Jin

PY - 2012

Y1 - 2012

N2 - In this paper, we propose a new approach to robust speaker identification using KPCA (kernel principal component analysis). This approach uses ensembles of classifiers (speaker identifiers) to reduce KPCA computation. KPCA enhances the features for each classifier. To reduce the processing time and memory requirements, we select a subset of limited number of samples randomly which is used as estimation set for each KPCA basis. The experimental result shows that the proposed approach shows better accuracy than PCA and GKPCA (greedy KPCA).

AB - In this paper, we propose a new approach to robust speaker identification using KPCA (kernel principal component analysis). This approach uses ensembles of classifiers (speaker identifiers) to reduce KPCA computation. KPCA enhances the features for each classifier. To reduce the processing time and memory requirements, we select a subset of limited number of samples randomly which is used as estimation set for each KPCA basis. The experimental result shows that the proposed approach shows better accuracy than PCA and GKPCA (greedy KPCA).

KW - classifier ensemble

KW - greedy kernel PCA

KW - speaker identification

UR - http://www.scopus.com/inward/record.url?scp=84863384676&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-28942-2_7

DO - 10.1007/978-3-642-28942-2_7

M3 - Conference contribution

AN - SCOPUS:84863384676

SN - 9783642289415

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 71

EP - 78

BT - Hybrid Artificial Intelligent Systems - 7th International Conference, HAIS 2012, Proceedings

T2 - 7th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2012

Y2 - 28 March 2012 through 30 March 2012

ER -

Yang IH, Kim MS, So BM, Kim MJ, Yu HJ. Robust speaker identification using ensembles of kernel principal component analysis. In Hybrid Artificial Intelligent Systems - 7th International Conference, HAIS 2012, Proceedings. PART 1 ed. 2012. p. 71-78. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-28942-2_7

Robust speaker identification using ensembles of kernel principal component analysis

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this