{"id":214912,"created":"2025-01-19T01:15:42.211177+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00214912","sets":["6504:10735:10808"]},"path":["10808"],"owner":"44499","recid":"214912","title":["音声中の音声検索語検出における平均事後確率ベクトル圧縮方式の検索精度改良"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-03-04"},"_buckets":{"deposit":"cc9e2741-5142-4591-9ccf-24a1e2c43feb"},"_deposit":{"id":"214912","pid":{"type":"depid","value":"214912","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"音声中の音声検索語検出における平均事後確率ベクトル圧縮方式の検索精度改良","author_link":["553101","553099","553097","553098","553100"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"音声中の音声検索語検出における平均事後確率ベクトル圧縮方式の検索精度改良"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"人工知能と認知科学","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2021-03-04","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"岩手県大"},{"subitem_text_value":"岩手県大"},{"subitem_text_value":"岩手県大"},{"subitem_text_value":"産総研"},{"subitem_text_value":"岩手県大"}]},"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/214912/files/IPSJ-Z83-6N-04.pdf","label":"IPSJ-Z83-6N-04.pdf"},"date":[{"dateType":"Available","dateValue":"2021-12-28"}],"format":"application/pdf","filename":"IPSJ-Z83-6N-04.pdf","filesize":[{"value":"554.6 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"5f566096-4efb-4a78-b2bd-21966c6cf8b2","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"横田, 平志"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"小嶋, 和徳"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"眞田, 尚久"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"李, 時旭"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"伊藤, 慶明"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"音声中の検索語検出において，DNNから算出した音声データの全事後確率ベクトル(Posteriorgram)を状態ごとの平均事後確率ベクトル(APPV)に圧縮することで，検索時のメモリ使用量の削減を行う方式を提案した．この方式では，音声データのPosteriorgramを求め，各フレームの事後確率ベクトルの要素の中で最も事後確率が高い要素(状態)を最尤状態とし、そのフレームにその状態番号を対応させる．同じ最尤状態を持つフレームの事後確率ベクトルをAPPVしている．最尤状態は学習モデルに依存するため，誤っていることも考えられる．本稿では，複数の学習モデルから求めた最尤状態を用いたAPPVの改良方式を提案する．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"218","bibliographic_titles":[{"bibliographic_title":"第83回全国大会講演論文集"}],"bibliographicPageStart":"217","bibliographicIssueDates":{"bibliographicIssueDate":"2021-03-04","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2021"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"updated":"2025-01-19T16:24:35.280977+00:00","links":{}}