{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00205209","sets":["6504:10247:10254"]},"path":["10254"],"owner":"6748","recid":"205209","title":["音声中の検索語検出における状態数の異なる複数の深層学習モデルを用いた検索精度の向上"],"pubdate":{"attribute_name":"公開日","attribute_value":"2020-02-20"},"_buckets":{"deposit":"7c714899-68b2-443e-9bea-eb4d25400ea9"},"_deposit":{"id":"205209","pid":{"type":"depid","value":"205209","revision_id":0},"owners":[6748],"status":"published","created_by":6748},"item_title":"音声中の検索語検出における状態数の異なる複数の深層学習モデルを用いた検索精度の向上","author_link":["509068","509067","509070","509069"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"音声中の検索語検出における状態数の異なる複数の深層学習モデルを用いた検索精度の向上"}]},"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":"2020-02-20","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"}]},"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/205209/files/IPSJ-Z82-4Q-04.pdf","label":"IPSJ-Z82-4Q-04.pdf"},"date":[{"dateType":"Available","dateValue":"2020-06-19"}],"format":"application/pdf","filename":"IPSJ-Z82-4Q-04.pdf","filesize":[{"value":"499.5 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"7b03dfdd-b0fe-4de4-8d91-c72208d3dd57","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2020 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"西野, 将弘"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"伊藤, 慶明"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"小嶋, 和徳"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"李, 時旭"}],"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":"近年、音声・動画データの大容量化と、それに伴うHDD等の記録媒体の高性能化により音声中からのキーワード検索機能の需要が増加している。先行研究では、音声データに対してモデルの事後確率からフレーム毎に最大値を算出した最尤系列を作成する音声データ最尤系列化方式を行うことで、Posteriorgram照合に比べ検索に要するメモリ容量を削減した。また、情報量削減に伴う検索精度低下の対策として、照合に複数の学習モデルを使用することで検索精度を向上した。本論文では、照合に事後確率の状態数が異なる複数の深層学習モデルを使用し、結果の検索スコアを統合することで、Posteriorgram照合から検索精度の向上を行う。","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"180","bibliographic_titles":[{"bibliographic_title":"第82回全国大会講演論文集"}],"bibliographicPageStart":"179","bibliographicIssueDates":{"bibliographicIssueDate":"2020-02-20","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2020"}]},"relation_version_is_last":true,"weko_creator_id":"6748"},"id":205209,"updated":"2025-01-19T19:52:03.090946+00:00","links":{},"created":"2025-01-19T01:07:22.681703+00:00"}