{"updated":"2025-01-20T00:02:04.103624+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00192714","sets":["1164:5159:9402:9617"]},"path":["9617"],"owner":"44499","recid":"192714","title":["Automatic Prediction of Symbolic and Sentence-Level Prosody in English for Development of a Reading Tutor"],"pubdate":{"attribute_name":"公開日","attribute_value":"2018-12-03"},"_buckets":{"deposit":"f50f4cd7-ad70-4ce2-84cc-fc4a6d514ea5"},"_deposit":{"id":"192714","pid":{"type":"depid","value":"192714","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Automatic Prediction of Symbolic and Sentence-Level Prosody in English for Development of a Reading Tutor","author_link":["450504","450505","450503","450506","450507","450502"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Automatic Prediction of Symbolic and Sentence-Level Prosody in English for Development of a Reading Tutor"},{"subitem_title":"Automatic Prediction of Symbolic and Sentence-Level Prosody in English for Development of a Reading Tutor","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"学生ポスターセッション","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2018-12-03","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"The University of Tokyo"},{"subitem_text_value":"The University of Tokyo"},{"subitem_text_value":"The University of Tokyo"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"The University of Tokyo","subitem_text_language":"en"},{"subitem_text_value":"The University of Tokyo","subitem_text_language":"en"},{"subitem_text_value":"The University of Tokyo","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/192714/files/IPSJ-SLP18125017.pdf","label":"IPSJ-SLP18125017.pdf"},"date":[{"dateType":"Available","dateValue":"2020-12-03"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-SLP18125017.pdf","filesize":[{"value":"650.5 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"22"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"e9319790-4092-4fd5-ba70-21f1c1ac052f","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2018 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Xinyi, Zhao"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Nobuaki, Minematsu"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Daisuke, Saito"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Xinyi, Zhao","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Nobuaki, Minematsu","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Daisuke, Saito","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10442647","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8663","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"In English education, speech synthesis technologies can be effectively used to develop a reading tutor to show students how to read given sentences in a natural and native way. The tutor can not only provide native-like audio of the input sentences but also visualize required prosodic structure to read those sentences aloud naturally. As the first step to develop such a reading tutor, prosodic events that can imply the intonation of the sentence need to be predicted from plain text. In this research, phrase boundary and 4-level stress instead of the traditional binary stress level are taken into consideration as prosodic events. 4-level stress labels not only categorize syllables into stressed ones and unstressed ones, but also indicate where phrase stress and sentence stress should appear in a sentence. Conditional Random Fields as a popular sequence labeling method are employed to do the prediction work. Experiments showed that applying our proposed method can improve the performance of prosody prediction compared to previous researches.","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In English education, speech synthesis technologies can be effectively used to develop a reading tutor to show students how to read given sentences in a natural and native way. The tutor can not only provide native-like audio of the input sentences but also visualize required prosodic structure to read those sentences aloud naturally. As the first step to develop such a reading tutor, prosodic events that can imply the intonation of the sentence need to be predicted from plain text. In this research, phrase boundary and 4-level stress instead of the traditional binary stress level are taken into consideration as prosodic events. 4-level stress labels not only categorize syllables into stressed ones and unstressed ones, but also indicate where phrase stress and sentence stress should appear in a sentence. Conditional Random Fields as a popular sequence labeling method are employed to do the prediction work. Experiments showed that applying our proposed method can improve the performance of prosody prediction compared to previous researches.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"4","bibliographic_titles":[{"bibliographic_title":"研究報告音声言語情報処理（SLP）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2018-12-03","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"17","bibliographicVolumeNumber":"2018-SLP-125"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T00:58:24.464521+00:00","id":192714,"links":{}}