Analysis and extraction of LP-residual for its application in speaker verification system under uncontrolled noisy environment

Sub-segmental analysis of excitation source may contain significant speaker-specific information pertaining to speaker verification. In this paper, the excitation source feature has been explored for design of speaker verification system (SVS). The baseline of the system is extraction of speaker-spe...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Multimedia tools and applications 2017, Vol.76 (1), p.757-784
Hauptverfasser: Misra, Songhita, Laskar, Rabul Hussain, Baruah, U., Das, T. K., Saha, P., Choudhury, S. P.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 784
container_issue 1
container_start_page 757
container_title Multimedia tools and applications
container_volume 76
creator Misra, Songhita
Laskar, Rabul Hussain
Baruah, U.
Das, T. K.
Saha, P.
Choudhury, S. P.
description Sub-segmental analysis of excitation source may contain significant speaker-specific information pertaining to speaker verification. In this paper, the excitation source feature has been explored for design of speaker verification system (SVS). The baseline of the system is extraction of speaker-specific information from LP-residual features by modelling the speakers through different supervised and unsupervised models, based on which they will be accepted or rejected. Direct LP-residual (DLR) as well as DCT coefficients of LP-residual (DCTLR) are approximated as the excitation source features for the system. The models are processed in two different level of analysis, namely, sentence level analysis as well as voice-segment level approach (VSLA), with the variations in the frame size of the input. Effects of the change of frame size in the input vectors are observed. Studies are carried over telephonic database collected in practical environment. A comparative analysis has been presented for the combination of models, features and the two levels of analysis for the given data. The experimental study suggests that application of VSLA on unsupervised models with DCTLR as input, provides a performance which is 14.21 % better than sentence level analysis of the models.
doi_str_mv 10.1007/s11042-015-3020-8
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1880006029</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>4297120721</sourcerecordid><originalsourceid>FETCH-LOGICAL-c301t-694be34acce2e9a1fd1f59147dd7a9666fc5b31b9e1482775956a28b3ca642603</originalsourceid><addsrcrecordid>eNp1kU2LFDEQhhtRcF39Ad4CXrzErUo6H31cFl2FAT3oOWTS1ZK1J2mT7sW5-NvNOC6I4KmKquetQz1d9xLhDQKYq4oIveCAiksQwO2j7gKVkdwYgY9bLy1wowCfds9qvQNArUR_0f28Tn4-1liZTyOjH2vxYY05sTyx3SdeqMZx8zObcmFxbdSyzDH430hMrC7kv1Fh91Ti9DCvx7rSgW1pbJsthZzWkueZRpZyrEdG6T6WnA6U1ufdk8nPlV78qZfdl3dvP9-857uPtx9urnc8SMCV66Hfk-x9CCRo8DiNOKkBezOOxg9a6ymovcT9QNhbYYwalPbC7mXwuhca5GX3-nx3Kfn7RnV1h1gDzbNPlLfq0FoA0CCGhr76B73LW2lfOlFKW7S6l43CMxVKrrXQ5JYSD74cHYI7GXFnI64ZcScjzraMOGdqY9NXKn9d_m_oF2lIkCM</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1856818643</pqid></control><display><type>article</type><title>Analysis and extraction of LP-residual for its application in speaker verification system under uncontrolled noisy environment</title><source>SpringerLink Journals - AutoHoldings</source><creator>Misra, Songhita ; Laskar, Rabul Hussain ; Baruah, U. ; Das, T. K. ; Saha, P. ; Choudhury, S. P.</creator><creatorcontrib>Misra, Songhita ; Laskar, Rabul Hussain ; Baruah, U. ; Das, T. K. ; Saha, P. ; Choudhury, S. P.</creatorcontrib><description>Sub-segmental analysis of excitation source may contain significant speaker-specific information pertaining to speaker verification. In this paper, the excitation source feature has been explored for design of speaker verification system (SVS). The baseline of the system is extraction of speaker-specific information from LP-residual features by modelling the speakers through different supervised and unsupervised models, based on which they will be accepted or rejected. Direct LP-residual (DLR) as well as DCT coefficients of LP-residual (DCTLR) are approximated as the excitation source features for the system. The models are processed in two different level of analysis, namely, sentence level analysis as well as voice-segment level approach (VSLA), with the variations in the frame size of the input. Effects of the change of frame size in the input vectors are observed. Studies are carried over telephonic database collected in practical environment. A comparative analysis has been presented for the combination of models, features and the two levels of analysis for the given data. The experimental study suggests that application of VSLA on unsupervised models with DCTLR as input, provides a performance which is 14.21 % better than sentence level analysis of the models.</description><identifier>ISSN: 1380-7501</identifier><identifier>EISSN: 1573-7721</identifier><identifier>DOI: 10.1007/s11042-015-3020-8</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Analysis ; Approximation ; Computer Communication Networks ; Computer Science ; Data Structures and Information Theory ; Design engineering ; Excitation ; Extraction ; Frames ; Identification systems ; Larynx ; Multimedia ; Multimedia computer applications ; Multimedia Information Systems ; Neural networks ; Noise ; Sentences ; Speakers ; Special Purpose and Application-Based Systems ; Speech ; Studies</subject><ispartof>Multimedia tools and applications, 2017, Vol.76 (1), p.757-784</ispartof><rights>Springer Science+Business Media New York 2015</rights><rights>Multimedia Tools and Applications is a copyright of Springer, 2017.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c301t-694be34acce2e9a1fd1f59147dd7a9666fc5b31b9e1482775956a28b3ca642603</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11042-015-3020-8$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11042-015-3020-8$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Misra, Songhita</creatorcontrib><creatorcontrib>Laskar, Rabul Hussain</creatorcontrib><creatorcontrib>Baruah, U.</creatorcontrib><creatorcontrib>Das, T. K.</creatorcontrib><creatorcontrib>Saha, P.</creatorcontrib><creatorcontrib>Choudhury, S. P.</creatorcontrib><title>Analysis and extraction of LP-residual for its application in speaker verification system under uncontrolled noisy environment</title><title>Multimedia tools and applications</title><addtitle>Multimed Tools Appl</addtitle><description>Sub-segmental analysis of excitation source may contain significant speaker-specific information pertaining to speaker verification. In this paper, the excitation source feature has been explored for design of speaker verification system (SVS). The baseline of the system is extraction of speaker-specific information from LP-residual features by modelling the speakers through different supervised and unsupervised models, based on which they will be accepted or rejected. Direct LP-residual (DLR) as well as DCT coefficients of LP-residual (DCTLR) are approximated as the excitation source features for the system. The models are processed in two different level of analysis, namely, sentence level analysis as well as voice-segment level approach (VSLA), with the variations in the frame size of the input. Effects of the change of frame size in the input vectors are observed. Studies are carried over telephonic database collected in practical environment. A comparative analysis has been presented for the combination of models, features and the two levels of analysis for the given data. The experimental study suggests that application of VSLA on unsupervised models with DCTLR as input, provides a performance which is 14.21 % better than sentence level analysis of the models.</description><subject>Analysis</subject><subject>Approximation</subject><subject>Computer Communication Networks</subject><subject>Computer Science</subject><subject>Data Structures and Information Theory</subject><subject>Design engineering</subject><subject>Excitation</subject><subject>Extraction</subject><subject>Frames</subject><subject>Identification systems</subject><subject>Larynx</subject><subject>Multimedia</subject><subject>Multimedia computer applications</subject><subject>Multimedia Information Systems</subject><subject>Neural networks</subject><subject>Noise</subject><subject>Sentences</subject><subject>Speakers</subject><subject>Special Purpose and Application-Based Systems</subject><subject>Speech</subject><subject>Studies</subject><issn>1380-7501</issn><issn>1573-7721</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>BENPR</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp1kU2LFDEQhhtRcF39Ad4CXrzErUo6H31cFl2FAT3oOWTS1ZK1J2mT7sW5-NvNOC6I4KmKquetQz1d9xLhDQKYq4oIveCAiksQwO2j7gKVkdwYgY9bLy1wowCfds9qvQNArUR_0f28Tn4-1liZTyOjH2vxYY05sTyx3SdeqMZx8zObcmFxbdSyzDH430hMrC7kv1Fh91Ti9DCvx7rSgW1pbJsthZzWkueZRpZyrEdG6T6WnA6U1ufdk8nPlV78qZfdl3dvP9-857uPtx9urnc8SMCV66Hfk-x9CCRo8DiNOKkBezOOxg9a6ymovcT9QNhbYYwalPbC7mXwuhca5GX3-nx3Kfn7RnV1h1gDzbNPlLfq0FoA0CCGhr76B73LW2lfOlFKW7S6l43CMxVKrrXQ5JYSD74cHYI7GXFnI64ZcScjzraMOGdqY9NXKn9d_m_oF2lIkCM</recordid><startdate>2017</startdate><enddate>2017</enddate><creator>Misra, Songhita</creator><creator>Laskar, Rabul Hussain</creator><creator>Baruah, U.</creator><creator>Das, T. K.</creator><creator>Saha, P.</creator><creator>Choudhury, S. P.</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SC</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>8AL</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8FL</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FRNLG</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K60</scope><scope>K6~</scope><scope>K7-</scope><scope>L.-</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0C</scope><scope>M0N</scope><scope>M2O</scope><scope>MBDVC</scope><scope>P5Z</scope><scope>P62</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope></search><sort><creationdate>2017</creationdate><title>Analysis and extraction of LP-residual for its application in speaker verification system under uncontrolled noisy environment</title><author>Misra, Songhita ; Laskar, Rabul Hussain ; Baruah, U. ; Das, T. K. ; Saha, P. ; Choudhury, S. P.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c301t-694be34acce2e9a1fd1f59147dd7a9666fc5b31b9e1482775956a28b3ca642603</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Analysis</topic><topic>Approximation</topic><topic>Computer Communication Networks</topic><topic>Computer Science</topic><topic>Data Structures and Information Theory</topic><topic>Design engineering</topic><topic>Excitation</topic><topic>Extraction</topic><topic>Frames</topic><topic>Identification systems</topic><topic>Larynx</topic><topic>Multimedia</topic><topic>Multimedia computer applications</topic><topic>Multimedia Information Systems</topic><topic>Neural networks</topic><topic>Noise</topic><topic>Sentences</topic><topic>Speakers</topic><topic>Special Purpose and Application-Based Systems</topic><topic>Speech</topic><topic>Studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Misra, Songhita</creatorcontrib><creatorcontrib>Laskar, Rabul Hussain</creatorcontrib><creatorcontrib>Baruah, U.</creatorcontrib><creatorcontrib>Das, T. K.</creatorcontrib><creatorcontrib>Saha, P.</creatorcontrib><creatorcontrib>Choudhury, S. P.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>Computer Science Database</collection><collection>ABI/INFORM Professional Advanced</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ABI/INFORM Global</collection><collection>Computing Database</collection><collection>Research Library</collection><collection>Research Library (Corporate)</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><jtitle>Multimedia tools and applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Misra, Songhita</au><au>Laskar, Rabul Hussain</au><au>Baruah, U.</au><au>Das, T. K.</au><au>Saha, P.</au><au>Choudhury, S. P.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Analysis and extraction of LP-residual for its application in speaker verification system under uncontrolled noisy environment</atitle><jtitle>Multimedia tools and applications</jtitle><stitle>Multimed Tools Appl</stitle><date>2017</date><risdate>2017</risdate><volume>76</volume><issue>1</issue><spage>757</spage><epage>784</epage><pages>757-784</pages><issn>1380-7501</issn><eissn>1573-7721</eissn><abstract>Sub-segmental analysis of excitation source may contain significant speaker-specific information pertaining to speaker verification. In this paper, the excitation source feature has been explored for design of speaker verification system (SVS). The baseline of the system is extraction of speaker-specific information from LP-residual features by modelling the speakers through different supervised and unsupervised models, based on which they will be accepted or rejected. Direct LP-residual (DLR) as well as DCT coefficients of LP-residual (DCTLR) are approximated as the excitation source features for the system. The models are processed in two different level of analysis, namely, sentence level analysis as well as voice-segment level approach (VSLA), with the variations in the frame size of the input. Effects of the change of frame size in the input vectors are observed. Studies are carried over telephonic database collected in practical environment. A comparative analysis has been presented for the combination of models, features and the two levels of analysis for the given data. The experimental study suggests that application of VSLA on unsupervised models with DCTLR as input, provides a performance which is 14.21 % better than sentence level analysis of the models.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11042-015-3020-8</doi><tpages>28</tpages></addata></record>
fulltext fulltext
identifier ISSN: 1380-7501
ispartof Multimedia tools and applications, 2017, Vol.76 (1), p.757-784
issn 1380-7501
1573-7721
language eng
recordid cdi_proquest_miscellaneous_1880006029
source SpringerLink Journals - AutoHoldings
subjects Analysis
Approximation
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Design engineering
Excitation
Extraction
Frames
Identification systems
Larynx
Multimedia
Multimedia computer applications
Multimedia Information Systems
Neural networks
Noise
Sentences
Speakers
Special Purpose and Application-Based Systems
Speech
Studies
title Analysis and extraction of LP-residual for its application in speaker verification system under uncontrolled noisy environment
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-31T19%3A41%3A44IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Analysis%20and%20extraction%20of%20LP-residual%20for%20its%20application%20in%20speaker%20verification%20system%20under%20uncontrolled%20noisy%20environment&rft.jtitle=Multimedia%20tools%20and%20applications&rft.au=Misra,%20Songhita&rft.date=2017&rft.volume=76&rft.issue=1&rft.spage=757&rft.epage=784&rft.pages=757-784&rft.issn=1380-7501&rft.eissn=1573-7721&rft_id=info:doi/10.1007/s11042-015-3020-8&rft_dat=%3Cproquest_cross%3E4297120721%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1856818643&rft_id=info:pmid/&rfr_iscdi=true