Findings of the 2024 Mandarin Stuttering Event Detection and Automatic Speech Recognition Challenge
The StutteringSpeech Challenge focuses on advancing speech technologies for people who stutter, specifically targeting Stuttering Event Detection (SED) and Automatic Speech Recognition (ASR) in Mandarin. The challenge comprises three tracks: (1) SED, which aims to develop systems for detection of st...
Gespeichert in:
| Veröffentlicht in: | arXiv.org 2024-09 |
|---|---|
| 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 | Xue, Hongfei Gong, Rong Shao, Mingchen Xu, Xin Wang, Lezhi Xie, Lei Bu, Hui Zhou, Jiaming Qin, Yong Du, Jun Li, Ming Zhang, Binbin Jia, Bin |
| description | The StutteringSpeech Challenge focuses on advancing speech technologies for people who stutter, specifically targeting Stuttering Event Detection (SED) and Automatic Speech Recognition (ASR) in Mandarin. The challenge comprises three tracks: (1) SED, which aims to develop systems for detection of stuttering events; (2) ASR, which focuses on creating robust systems for recognizing stuttered speech; and (3) Research track for innovative approaches utilizing the provided dataset. We utilizes an open-source Mandarin stuttering dataset AS-70, which has been split into new training and test sets for the challenge. This paper presents the dataset, details the challenge tracks, and analyzes the performance of the top systems, highlighting improvements in detection accuracy and reductions in recognition error rates. Our findings underscore the potential of specialized models and augmentation strategies in developing stuttered speech technologies. |
| format | Article |
| fullrecord | <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_3102578437</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3102578437</sourcerecordid><originalsourceid>FETCH-proquest_journals_31025784373</originalsourceid><addsrcrecordid>eNqNykEKwjAQheEgCBbtHQZcCzFpbbeiFTdu1L2EdEwjdVKbqee3iAdw9X5430QkSuv1qsyUmok0xoeUUm0Klec6EfbgqfbkIoQ7cIOgpMrgZKg2vSe48MCMYzmo3kgMe2S07APBSGA7cHga9hYuHaJt4Iw2OPJfsGtM2yI5XIjp3bQR09_OxfJQXXfHVdeH14CRb48w9DReN72WKi_KTBf6P_UBHlBFAg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3102578437</pqid></control><display><type>article</type><title>Findings of the 2024 Mandarin Stuttering Event Detection and Automatic Speech Recognition Challenge</title><source>Free E- Journals</source><creator>Xue, Hongfei ; Gong, Rong ; Shao, Mingchen ; Xu, Xin ; Wang, Lezhi ; Xie, Lei ; Bu, Hui ; Zhou, Jiaming ; Qin, Yong ; Du, Jun ; Li, Ming ; Zhang, Binbin ; Jia, Bin</creator><creatorcontrib>Xue, Hongfei ; Gong, Rong ; Shao, Mingchen ; Xu, Xin ; Wang, Lezhi ; Xie, Lei ; Bu, Hui ; Zhou, Jiaming ; Qin, Yong ; Du, Jun ; Li, Ming ; Zhang, Binbin ; Jia, Bin</creatorcontrib><description>The StutteringSpeech Challenge focuses on advancing speech technologies for people who stutter, specifically targeting Stuttering Event Detection (SED) and Automatic Speech Recognition (ASR) in Mandarin. The challenge comprises three tracks: (1) SED, which aims to develop systems for detection of stuttering events; (2) ASR, which focuses on creating robust systems for recognizing stuttered speech; and (3) Research track for innovative approaches utilizing the provided dataset. We utilizes an open-source Mandarin stuttering dataset AS-70, which has been split into new training and test sets for the challenge. This paper presents the dataset, details the challenge tracks, and analyzes the performance of the top systems, highlighting improvements in detection accuracy and reductions in recognition error rates. Our findings underscore the potential of specialized models and augmentation strategies in developing stuttered speech technologies.</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Automatic speech recognition ; Datasets ; Error analysis ; Error detection</subject><ispartof>arXiv.org, 2024-09</ispartof><rights>2024. 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>Xue, Hongfei</creatorcontrib><creatorcontrib>Gong, Rong</creatorcontrib><creatorcontrib>Shao, Mingchen</creatorcontrib><creatorcontrib>Xu, Xin</creatorcontrib><creatorcontrib>Wang, Lezhi</creatorcontrib><creatorcontrib>Xie, Lei</creatorcontrib><creatorcontrib>Bu, Hui</creatorcontrib><creatorcontrib>Zhou, Jiaming</creatorcontrib><creatorcontrib>Qin, Yong</creatorcontrib><creatorcontrib>Du, Jun</creatorcontrib><creatorcontrib>Li, Ming</creatorcontrib><creatorcontrib>Zhang, Binbin</creatorcontrib><creatorcontrib>Jia, Bin</creatorcontrib><title>Findings of the 2024 Mandarin Stuttering Event Detection and Automatic Speech Recognition Challenge</title><title>arXiv.org</title><description>The StutteringSpeech Challenge focuses on advancing speech technologies for people who stutter, specifically targeting Stuttering Event Detection (SED) and Automatic Speech Recognition (ASR) in Mandarin. The challenge comprises three tracks: (1) SED, which aims to develop systems for detection of stuttering events; (2) ASR, which focuses on creating robust systems for recognizing stuttered speech; and (3) Research track for innovative approaches utilizing the provided dataset. We utilizes an open-source Mandarin stuttering dataset AS-70, which has been split into new training and test sets for the challenge. This paper presents the dataset, details the challenge tracks, and analyzes the performance of the top systems, highlighting improvements in detection accuracy and reductions in recognition error rates. Our findings underscore the potential of specialized models and augmentation strategies in developing stuttered speech technologies.</description><subject>Automatic speech recognition</subject><subject>Datasets</subject><subject>Error analysis</subject><subject>Error detection</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNqNykEKwjAQheEgCBbtHQZcCzFpbbeiFTdu1L2EdEwjdVKbqee3iAdw9X5430QkSuv1qsyUmok0xoeUUm0Klec6EfbgqfbkIoQ7cIOgpMrgZKg2vSe48MCMYzmo3kgMe2S07APBSGA7cHga9hYuHaJt4Iw2OPJfsGtM2yI5XIjp3bQR09_OxfJQXXfHVdeH14CRb48w9DReN72WKi_KTBf6P_UBHlBFAg</recordid><startdate>20240909</startdate><enddate>20240909</enddate><creator>Xue, Hongfei</creator><creator>Gong, Rong</creator><creator>Shao, Mingchen</creator><creator>Xu, Xin</creator><creator>Wang, Lezhi</creator><creator>Xie, Lei</creator><creator>Bu, Hui</creator><creator>Zhou, Jiaming</creator><creator>Qin, Yong</creator><creator>Du, Jun</creator><creator>Li, Ming</creator><creator>Zhang, Binbin</creator><creator>Jia, Bin</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>20240909</creationdate><title>Findings of the 2024 Mandarin Stuttering Event Detection and Automatic Speech Recognition Challenge</title><author>Xue, Hongfei ; Gong, Rong ; Shao, Mingchen ; Xu, Xin ; Wang, Lezhi ; Xie, Lei ; Bu, Hui ; Zhou, Jiaming ; Qin, Yong ; Du, Jun ; Li, Ming ; Zhang, Binbin ; Jia, Bin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_31025784373</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Automatic speech recognition</topic><topic>Datasets</topic><topic>Error analysis</topic><topic>Error detection</topic><toplevel>online_resources</toplevel><creatorcontrib>Xue, Hongfei</creatorcontrib><creatorcontrib>Gong, Rong</creatorcontrib><creatorcontrib>Shao, Mingchen</creatorcontrib><creatorcontrib>Xu, Xin</creatorcontrib><creatorcontrib>Wang, Lezhi</creatorcontrib><creatorcontrib>Xie, Lei</creatorcontrib><creatorcontrib>Bu, Hui</creatorcontrib><creatorcontrib>Zhou, Jiaming</creatorcontrib><creatorcontrib>Qin, Yong</creatorcontrib><creatorcontrib>Du, Jun</creatorcontrib><creatorcontrib>Li, Ming</creatorcontrib><creatorcontrib>Zhang, Binbin</creatorcontrib><creatorcontrib>Jia, Bin</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>Xue, Hongfei</au><au>Gong, Rong</au><au>Shao, Mingchen</au><au>Xu, Xin</au><au>Wang, Lezhi</au><au>Xie, Lei</au><au>Bu, Hui</au><au>Zhou, Jiaming</au><au>Qin, Yong</au><au>Du, Jun</au><au>Li, Ming</au><au>Zhang, Binbin</au><au>Jia, Bin</au><format>book</format><genre>document</genre><ristype>GEN</ristype><atitle>Findings of the 2024 Mandarin Stuttering Event Detection and Automatic Speech Recognition Challenge</atitle><jtitle>arXiv.org</jtitle><date>2024-09-09</date><risdate>2024</risdate><eissn>2331-8422</eissn><abstract>The StutteringSpeech Challenge focuses on advancing speech technologies for people who stutter, specifically targeting Stuttering Event Detection (SED) and Automatic Speech Recognition (ASR) in Mandarin. The challenge comprises three tracks: (1) SED, which aims to develop systems for detection of stuttering events; (2) ASR, which focuses on creating robust systems for recognizing stuttered speech; and (3) Research track for innovative approaches utilizing the provided dataset. We utilizes an open-source Mandarin stuttering dataset AS-70, which has been split into new training and test sets for the challenge. This paper presents the dataset, details the challenge tracks, and analyzes the performance of the top systems, highlighting improvements in detection accuracy and reductions in recognition error rates. Our findings underscore the potential of specialized models and augmentation strategies in developing stuttered speech technologies.</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, 2024-09 |
| issn | 2331-8422 |
| language | eng |
| recordid | cdi_proquest_journals_3102578437 |
| source | Free E- Journals |
| subjects | Automatic speech recognition Datasets Error analysis Error detection |
| title | Findings of the 2024 Mandarin Stuttering Event Detection and Automatic Speech Recognition Challenge |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-08T01%3A26%3A32IST&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=Findings%20of%20the%202024%20Mandarin%20Stuttering%20Event%20Detection%20and%20Automatic%20Speech%20Recognition%20Challenge&rft.jtitle=arXiv.org&rft.au=Xue,%20Hongfei&rft.date=2024-09-09&rft.eissn=2331-8422&rft_id=info:doi/&rft_dat=%3Cproquest%3E3102578437%3C/proquest%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3102578437&rft_id=info:pmid/&rfr_iscdi=true |