Native Language Identification using i-vector
The task of determining a speaker's native language based only on his speeches in a second language is known as Native Language Identification or NLI. Due to its increasing applications in various domains of speech signal processing, this has emerged as an important research area in recent time...
Gespeichert in:
Veröffentlicht in: | arXiv.org 2018-11 |
---|---|
Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | |
container_start_page | |
container_title | arXiv.org |
container_volume | |
creator | Ahmed Nazim Uddin Rahman, Md Ashequr Islam, Md Rafidul Haque, Mohammad Ariful |
description | The task of determining a speaker's native language based only on his speeches in a second language is known as Native Language Identification or NLI. Due to its increasing applications in various domains of speech signal processing, this has emerged as an important research area in recent times. In this paper we have proposed an i-vector based approach to develop an automatic NLI system using MFCC and GFCC features. For evaluation of our approach, we have tested our framework on the 2016 ComParE Native language sub-challenge dataset which has English language speakers from 11 different native language backgrounds. Our proposed method outperforms the baseline system with an improvement in accuracy by 21.95% for the MFCC feature based i-vector framework and 22.81% for the GFCC feature based i-vector framework. |
format | Article |
fullrecord | <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_2133668388</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2133668388</sourcerecordid><originalsourceid>FETCH-proquest_journals_21336683883</originalsourceid><addsrcrecordid>eNpjYuA0MjY21LUwMTLiYOAtLs4yMDAwMjM3MjU15mTQ9UssySxLVfBJzEsvTUxPVfBMSc0ryUzLTAaK5-cplBZn5qUrZOqWpSaX5BfxMLCmJeYUp_JCaW4GZTfXEGcP3YKi_MLS1OKS-Kz80qI8oFS8kaGxsZmZhbGFhTFxqgDCdjIW</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2133668388</pqid></control><display><type>article</type><title>Native Language Identification using i-vector</title><source>Free E- Journals</source><creator>Ahmed Nazim Uddin ; Rahman, Md Ashequr ; Islam, Md Rafidul ; Haque, Mohammad Ariful</creator><creatorcontrib>Ahmed Nazim Uddin ; Rahman, Md Ashequr ; Islam, Md Rafidul ; Haque, Mohammad Ariful</creatorcontrib><description>The task of determining a speaker's native language based only on his speeches in a second language is known as Native Language Identification or NLI. Due to its increasing applications in various domains of speech signal processing, this has emerged as an important research area in recent times. In this paper we have proposed an i-vector based approach to develop an automatic NLI system using MFCC and GFCC features. For evaluation of our approach, we have tested our framework on the 2016 ComParE Native language sub-challenge dataset which has English language speakers from 11 different native language backgrounds. Our proposed method outperforms the baseline system with an improvement in accuracy by 21.95% for the MFCC feature based i-vector framework and 22.81% for the GFCC feature based i-vector framework.</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Domains ; English as a second language ; English language ; Native languages ; Signal processing</subject><ispartof>arXiv.org, 2018-11</ispartof><rights>2018. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>780,784</link.rule.ids></links><search><creatorcontrib>Ahmed Nazim Uddin</creatorcontrib><creatorcontrib>Rahman, Md Ashequr</creatorcontrib><creatorcontrib>Islam, Md Rafidul</creatorcontrib><creatorcontrib>Haque, Mohammad Ariful</creatorcontrib><title>Native Language Identification using i-vector</title><title>arXiv.org</title><description>The task of determining a speaker's native language based only on his speeches in a second language is known as Native Language Identification or NLI. Due to its increasing applications in various domains of speech signal processing, this has emerged as an important research area in recent times. In this paper we have proposed an i-vector based approach to develop an automatic NLI system using MFCC and GFCC features. For evaluation of our approach, we have tested our framework on the 2016 ComParE Native language sub-challenge dataset which has English language speakers from 11 different native language backgrounds. Our proposed method outperforms the baseline system with an improvement in accuracy by 21.95% for the MFCC feature based i-vector framework and 22.81% for the GFCC feature based i-vector framework.</description><subject>Domains</subject><subject>English as a second language</subject><subject>English language</subject><subject>Native languages</subject><subject>Signal processing</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpjYuA0MjY21LUwMTLiYOAtLs4yMDAwMjM3MjU15mTQ9UssySxLVfBJzEsvTUxPVfBMSc0ryUzLTAaK5-cplBZn5qUrZOqWpSaX5BfxMLCmJeYUp_JCaW4GZTfXEGcP3YKi_MLS1OKS-Kz80qI8oFS8kaGxsZmZhbGFhTFxqgDCdjIW</recordid><startdate>20181109</startdate><enddate>20181109</enddate><creator>Ahmed Nazim Uddin</creator><creator>Rahman, Md Ashequr</creator><creator>Islam, Md Rafidul</creator><creator>Haque, Mohammad Ariful</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20181109</creationdate><title>Native Language Identification using i-vector</title><author>Ahmed Nazim Uddin ; Rahman, Md Ashequr ; Islam, Md Rafidul ; Haque, Mohammad Ariful</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_21336683883</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Domains</topic><topic>English as a second language</topic><topic>English language</topic><topic>Native languages</topic><topic>Signal processing</topic><toplevel>online_resources</toplevel><creatorcontrib>Ahmed Nazim Uddin</creatorcontrib><creatorcontrib>Rahman, Md Ashequr</creatorcontrib><creatorcontrib>Islam, Md Rafidul</creatorcontrib><creatorcontrib>Haque, Mohammad Ariful</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</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 China</collection><collection>Engineering Collection</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ahmed Nazim Uddin</au><au>Rahman, Md Ashequr</au><au>Islam, Md Rafidul</au><au>Haque, Mohammad Ariful</au><format>book</format><genre>document</genre><ristype>GEN</ristype><atitle>Native Language Identification using i-vector</atitle><jtitle>arXiv.org</jtitle><date>2018-11-09</date><risdate>2018</risdate><eissn>2331-8422</eissn><abstract>The task of determining a speaker's native language based only on his speeches in a second language is known as Native Language Identification or NLI. Due to its increasing applications in various domains of speech signal processing, this has emerged as an important research area in recent times. In this paper we have proposed an i-vector based approach to develop an automatic NLI system using MFCC and GFCC features. For evaluation of our approach, we have tested our framework on the 2016 ComParE Native language sub-challenge dataset which has English language speakers from 11 different native language backgrounds. Our proposed method outperforms the baseline system with an improvement in accuracy by 21.95% for the MFCC feature based i-vector framework and 22.81% for the GFCC feature based i-vector framework.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | EISSN: 2331-8422 |
ispartof | arXiv.org, 2018-11 |
issn | 2331-8422 |
language | eng |
recordid | cdi_proquest_journals_2133668388 |
source | Free E- Journals |
subjects | Domains English as a second language English language Native languages Signal processing |
title | Native Language Identification using i-vector |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-19T08%3A18%3A18IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=document&rft.atitle=Native%20Language%20Identification%20using%20i-vector&rft.jtitle=arXiv.org&rft.au=Ahmed%20Nazim%20Uddin&rft.date=2018-11-09&rft.eissn=2331-8422&rft_id=info:doi/&rft_dat=%3Cproquest%3E2133668388%3C/proquest%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2133668388&rft_id=info:pmid/&rfr_iscdi=true |