Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 장준혁 | - |
dc.date.accessioned | 2019-11-18T07:44:18Z | - |
dc.date.available | 2019-11-18T07:44:18Z | - |
dc.date.issued | 2017-01 | - |
dc.identifier.citation | 대한전자공학회 학술대회, page. 830-831 | en_US |
dc.identifier.uri | http://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE07110689 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/112201 | - |
dc.description.abstract | In this paper, we employ several model-spaceadaptation techniques for deep neural network (DNN)based voice activity detection (VAD) to adapt themodel to unseen background noise conditions.Adaptation results are evaluated in terms of theobjective measures such as frame accuracy, speechhit rate (HIT), and false alarm rate (FA).Experimental results suggest that the adaptation hasbeen carried out mainly to learn the noise-specificcharacteristics, rather than modeling the speechrelated features of the unseen adaptation utterances. | en_US |
dc.description.sponsorship | This work was supported by the National ResearchFoundation of Korea (NRF) grant funded by theKorea government (MSIP) (No.2014R1A2A1A10049735). | en_US |
dc.language.iso | en | en_US |
dc.publisher | 대한전자공학회 | en_US |
dc.subject | voice activity detection | en_US |
dc.subject | deep neural network | en_US |
dc.subject | model-space adaptation | en_US |
dc.title | Experimental study on applying adaptation techniques for deep neural network based voice activity detection | en_US |
dc.type | Article | en_US |
dc.relation.page | 1-2 | - |
dc.contributor.googleauthor | Yang, Joon-Young | - |
dc.contributor.googleauthor | Chang, Joon-Hyuk | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | DEPARTMENT OF ELECTRONIC ENGINEERING | - |
dc.identifier.pid | jchang | - |
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