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https://hdl.handle.net/20.500.11779/1568
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Fakhan, Enver | - |
dc.contributor.author | Arısoy, Ebru | - |
dc.date.accessioned | 2021-10-09T07:19:14Z | |
dc.date.available | 2021-10-09T07:19:14Z | |
dc.date.issued | 2020 | - |
dc.identifier.citation | E. Fakhan and E. Arısoy, (5-7 Oct. 2020). Domain Adaptation Approaches for Acoustic Modeling," 2020 28th Signal Processing and Communications Applications Conference (SIU), pp. 1-4, doi: 10.1109/SIU49456.2020.9302343. | en_US |
dc.identifier.isbn | 9781728172064 | - |
dc.identifier.issn | 2165-0608 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11779/1568 | - |
dc.identifier.uri | https://doi.org/10.1109/SIU49456.2020.9302343 | - |
dc.description.abstract | In the recent years, with the development of neural network based models, ASR systems have achieved a tremendous performance increase. However, this performance increase mostly depends on the amount of training data and the computational power. In a low-resource data scenario, publicly available datasets can be utilized to overcome data scarcity. Furthermore, using a pre-trained model and adapting it to the in-domain data can help with computational constraint. In this paper we have leveraged two different publicly available datasets and investigate various acoustic model adaptation approaches. We show that 4% word error rate can be achieved using a very limited in-domain data. | en_US |
dc.description.sponsorship | Istanbul Medipol Univ | en_US |
dc.language.iso | tr | en_US |
dc.publisher | IEEE | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Adaptation models | en_US |
dc.subject | Computational modeling | en_US |
dc.subject | Neural networks | en_US |
dc.subject | Data models | en_US |
dc.subject | Art | en_US |
dc.subject | Transforms | en_US |
dc.subject | Training data | en_US |
dc.subject | Akustik model uyarlama | en_US |
dc.subject | Otomatik konuşma tanıma | en_US |
dc.subject | Yapay sinir ağları | en_US |
dc.title | Domain adaptation approaches for acoustic modeling | en_US |
dc.title.alternative | Akustik modelleme için alana uyarlama yaklaşımları | en_US |
dc.type | Conference Object | en_US |
dc.identifier.doi | 10.1109/SIU49456.2020.9302343 | - |
dc.identifier.scopus | 2-s2.0-85100309893 | en_US |
dc.authorid | Ebru Arısoy / 0000-0002-8311-3611 | - |
dc.description.woscitationindex | Conference Proceedings Citation Index - Science | - |
dc.description.WoSDocumentType | Proceedings Paper | |
dc.description.WoSInternationalCollaboration | Uluslararası işbirliği ile yapılmayan - HAYIR | en_US |
dc.description.WoSPublishedMonth | October | en_US |
dc.description.WoSIndexDate | 2020 | en_US |
dc.description.WoSYOKperiod | YÖK - 2020-21 | en_US |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.identifier.startpage | 1-4 | en_US |
dc.department | Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümü | en_US |
dc.relation.journal | 2020 28th Signal Processing and Communications Applications Conference (SIU) | en_US |
dc.identifier.wos | WOS:000653136100316 | en_US |
dc.institutionauthor | Arısoy, Ebru | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.grantfulltext | embargo_20400101 | - |
item.languageiso639-1 | tr | - |
item.cerifentitytype | Publications | - |
item.fulltext | With Fulltext | - |
item.openairetype | Conference Object | - |
Appears in Collections: | Elektrik Elektronik Mühendisliği Bölümü koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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File | Description | Size | Format | |
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Domain_Adaptation_Approaches_for_Acoustic_Modeling.pdf Until 2040-01-01 | Proceedings Paper | 503.49 kB | Adobe PDF | View/Open Request a copy |
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