{"links":{},"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00236041","sets":["6504:11678:11689"]},"path":["11689"],"owner":"44499","recid":"236041","title":["原言語テキストを補助入力とするTransformer同時通訳音声認識における大規模機械翻訳コーパスを用いた事前学習の検討"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-03-01"},"_buckets":{"deposit":"4f53ec16-95a0-4ca2-8e73-17fc2f2729cb"},"_deposit":{"id":"236041","pid":{"type":"depid","value":"236041","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"原言語テキストを補助入力とするTransformer同時通訳音声認識における大規模機械翻訳コーパスを用いた事前学習の検討","author_link":["645075","645074","645073","645072"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"原言語テキストを補助入力とするTransformer同時通訳音声認識における大規模機械翻訳コーパスを用いた事前学習の検討"}]},"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":"2024-03-01","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":"同志社大"},{"subitem_text_value":"同志社大"},{"subitem_text_value":"同志社大"},{"subitem_text_value":"マインドワード株式会社"}]},"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/236041/files/IPSJ-Z86-4R-05.pdf","label":"IPSJ-Z86-4R-05.pdf"},"date":[{"dateType":"Available","dateValue":"2024-07-03"}],"format":"application/pdf","filename":"IPSJ-Z86-4R-05.pdf","filesize":[{"value":"406.1 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"c097b1ce-2a05-49c1-8dbc-e4040dd7373c","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"谷口, 秀太"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"加藤, 恒夫"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"田村, 晃裕"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"安田, 圭志"}],"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":"同時通訳の音声認識は，フィラーや言い淀み，言い直しが含まれるため簡単ではない．筆者らは同時通訳の音声認識のために，原言語テキストを補助入力とするTransformer音声認識を提案してきた．これまで，提案モデルの学習に必要となる大規模な音声・書き起こし・原言語テキストの三つ組データとして，音声翻訳開発用のMust-Cコーパスを転用してきたが，原言語テキストエンコーダの学習用コーパスとしては十分でなかった．そこで，より大規模な機械翻訳開発用のWMTコーパスを用いて提案モデルの事前学習を行った．実験の結果，同時通訳音声認識においてWERを0.7ポイント削減した．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"398","bibliographic_titles":[{"bibliographic_title":"第86回全国大会講演論文集"}],"bibliographicPageStart":"397","bibliographicIssueDates":{"bibliographicIssueDate":"2024-03-01","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2024"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:37:52.682722+00:00","updated":"2025-01-19T09:25:07.342330+00:00","id":236041}