WEKO3
アイテム
{"_buckets": {"deposit": "7035fa8b-e8a8-4b3d-a19f-0d73d518f96b"}, "_deposit": {"id": "7746", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "7746"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00007746", "sets": ["322"]}, "author_link": ["21975", "21976", "21977", "21978"], "item_10_biblio_info_6": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2007", "bibliographicIssueDateType": "Issued"}, "bibliographicPageEnd": "2027", "bibliographicPageStart": "2022", "bibliographic_titles": [{"bibliographic_title": "IEEE International Conference on Systems, Man and Cybernetics", "bibliographic_titleLang": "en"}]}]}, "item_10_description_4": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "The focus of this paper is mental tension detection in speech to assist control the tension in day-to-day business such as conferences and operations in a call center. It is difficult to use classical techniques for mental tension detection in day-to-day business because those techniques require invasion body by electrodes or squirts and tied up by cables. In order to achieve a non-invasive, non-contact and low-restricting method, this proposed technique uses acoustic features in the speech. The technique uses the vocal tract model which represents the shape and the tightness of throat muscle. The Gaussian Mixture Model (GMM) classifies two mental tension states: high-tension and non-tension. The experiment result shows high recognition rate of mental tension detection.", "subitem_description_language": "en", "subitem_description_type": "Abstract"}]}, "item_10_identifier_60": {"attribute_name": "URI", "attribute_value_mlt": [{"subitem_identifier_type": "HDL", "subitem_identifier_uri": "http://hdl.handle.net/2237/9466"}]}, "item_10_publisher_32": {"attribute_name": "出版者", "attribute_value_mlt": [{"subitem_publisher": "IEEE", "subitem_publisher_language": "en"}]}, "item_10_relation_11": {"attribute_name": "DOI", "attribute_value_mlt": [{"subitem_relation_type": "isVersionOf", "subitem_relation_type_id": {"subitem_relation_type_id_text": "https://doi.org/10.1109/ICSMC.2007.4414150", "subitem_relation_type_select": "DOI"}}]}, "item_10_relation_8": {"attribute_name": "ISBN", "attribute_value_mlt": [{"subitem_relation_type": "isPartOf", "subitem_relation_type_id": {"subitem_relation_type_id_text": "978-1-4244-0991-4", "subitem_relation_type_select": "ISBN"}}]}, "item_10_rights_12": {"attribute_name": "権利", "attribute_value_mlt": [{"subitem_rights": "Copyright © 2007 IEEE. Reprinted from (relevant publication info). This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Nagoya University’s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org.", "subitem_rights_language": "en"}]}, "item_10_select_15": {"attribute_name": "著者版フラグ", "attribute_value_mlt": [{"subitem_select_item": "publisher"}]}, "item_10_text_14": {"attribute_name": "フォーマット", "attribute_value_mlt": [{"subitem_text_value": "application/pdf"}]}, "item_1615787544753": {"attribute_name": "出版タイプ", "attribute_value_mlt": [{"subitem_version_resource": "http://purl.org/coar/version/c_970fb48d4fbd8a85", "subitem_version_type": "VoR"}]}, "item_access_right": {"attribute_name": "アクセス権", "attribute_value_mlt": [{"subitem_access_right": "open access", "subitem_access_right_uri": "http://purl.org/coar/access_right/c_abf2"}]}, "item_creator": {"attribute_name": "著者", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "Ariga, Michiaki", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "21975", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Yano, Yoshikazu", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "21976", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Doki, Shinji", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "21977", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Okuma, Shigeru", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "21978", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "ファイル情報", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2018-02-19"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "doki_1.pdf", "filesize": [{"value": "190.5 kB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_note", "mimetype": "application/pdf", "size": 190500.0, "url": {"label": "doki_1.pdf", "objectType": "fulltext", "url": "https://nagoya.repo.nii.ac.jp/record/7746/files/doki_1.pdf"}, "version_id": "2f8f30ee-f035-4027-b0af-30418fdccb76"}]}, "item_language": {"attribute_name": "言語", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "item_resource_type": {"attribute_name": "資源タイプ", "attribute_value_mlt": [{"resourcetype": "journal article", "resourceuri": "http://purl.org/coar/resource_type/c_6501"}]}, "item_title": "Mental Tension Detection in the Speech based on physiological monitoring", "item_titles": {"attribute_name": "タイトル", "attribute_value_mlt": [{"subitem_title": "Mental Tension Detection in the Speech based on physiological monitoring", "subitem_title_language": "en"}]}, "item_type_id": "10", "owner": "1", "path": ["322"], "permalink_uri": "http://hdl.handle.net/2237/9466", "pubdate": {"attribute_name": "PubDate", "attribute_value": "2008-02-25"}, "publish_date": "2008-02-25", "publish_status": "0", "recid": "7746", "relation": {}, "relation_version_is_last": true, "title": ["Mental Tension Detection in the Speech based on physiological monitoring"], "weko_shared_id": -1}
Mental Tension Detection in the Speech based on physiological monitoring
http://hdl.handle.net/2237/9466
http://hdl.handle.net/2237/9466111c144a-8f5b-41ac-b664-9fea620faa4f
名前 / ファイル | ライセンス | アクション |
---|---|---|
doki_1.pdf (190.5 kB)
|
|
Item type | 学術雑誌論文 / Journal Article(1) | |||||
---|---|---|---|---|---|---|
公開日 | 2008-02-25 | |||||
タイトル | ||||||
タイトル | Mental Tension Detection in the Speech based on physiological monitoring | |||||
言語 | en | |||||
著者 |
Ariga, Michiaki
× Ariga, Michiaki× Yano, Yoshikazu× Doki, Shinji× Okuma, Shigeru |
|||||
アクセス権 | ||||||
アクセス権 | open access | |||||
アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||
権利 | ||||||
言語 | en | |||||
権利情報 | Copyright © 2007 IEEE. Reprinted from (relevant publication info). This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Nagoya University’s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. | |||||
抄録 | ||||||
内容記述 | The focus of this paper is mental tension detection in speech to assist control the tension in day-to-day business such as conferences and operations in a call center. It is difficult to use classical techniques for mental tension detection in day-to-day business because those techniques require invasion body by electrodes or squirts and tied up by cables. In order to achieve a non-invasive, non-contact and low-restricting method, this proposed technique uses acoustic features in the speech. The technique uses the vocal tract model which represents the shape and the tightness of throat muscle. The Gaussian Mixture Model (GMM) classifies two mental tension states: high-tension and non-tension. The experiment result shows high recognition rate of mental tension detection. | |||||
言語 | en | |||||
内容記述タイプ | Abstract | |||||
出版者 | ||||||
言語 | en | |||||
出版者 | IEEE | |||||
言語 | ||||||
言語 | eng | |||||
資源タイプ | ||||||
資源タイプresource | http://purl.org/coar/resource_type/c_6501 | |||||
タイプ | journal article | |||||
出版タイプ | ||||||
出版タイプ | VoR | |||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||
DOI | ||||||
関連タイプ | isVersionOf | |||||
識別子タイプ | DOI | |||||
関連識別子 | https://doi.org/10.1109/ICSMC.2007.4414150 | |||||
ISBN | ||||||
関連タイプ | isPartOf | |||||
識別子タイプ | ISBN | |||||
関連識別子 | 978-1-4244-0991-4 | |||||
書誌情報 |
en : IEEE International Conference on Systems, Man and Cybernetics p. 2022-2027, 発行日 2007 |
|||||
フォーマット | ||||||
application/pdf | ||||||
著者版フラグ | ||||||
値 | publisher | |||||
URI | ||||||
識別子 | http://hdl.handle.net/2237/9466 | |||||
識別子タイプ | HDL |