{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00216642","sets":["1164:5159:10869:10870"]},"path":["10870"],"owner":"44499","recid":"216642","title":["ソース・フィルタ・チャネル分解に基づく自己教師ありニューラル音声復元"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-02-22"},"_buckets":{"deposit":"c8cc094c-3dcf-4117-a3f7-7cb06e9e4d2f"},"_deposit":{"id":"216642","pid":{"type":"depid","value":"216642","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"ソース・フィルタ・チャネル分解に基づく自己教師ありニューラル音声復元","author_link":["559484","559481","559476","559477","559483","559480","559478","559479","559482","559475"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"ソース・フィルタ・チャネル分解に基づく自己教師ありニューラル音声復元"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"ポスターセッション3","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2022-02-22","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東京大学大学院情報理工学系研究科"},{"subitem_text_value":"東京大学大学院情報理工学系研究科"},{"subitem_text_value":"東京大学大学院情報理工学系研究科"},{"subitem_text_value":"東京大学大学院情報理工学系研究科"},{"subitem_text_value":"東京大学大学院情報理工学系研究科"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate 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高明"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"高道, 慎之介"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"中村, 友彦"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"丹治, 尚子"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"猿渡, 洋"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Takaaki, Saeki","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Shinnosuke, Takamichi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tomohiko, Nakamura","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Naoko, Tanji","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hiroshi, Saruwatari","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":"現代の音声工学研究に用いられる音声データは,一般に高品質な録音機器や整備された環境で収録されるが,過去の年代の録音音声などの低品質な音声データの活用が求められる場面も多い.しかし,当該録音の話者の高品質データや録音機器の情報が得られないため,劣化音声と高品質音声との対データを用いて音声復元モデルを教師あり学習することは困難である.そこで,本研究では,劣化音声データのみを用いた自己教師ありニューラル音声復元手法を提案する.提案手法は,劣化音声から復元音声の音声特徴量を推定する分析モジュール,音声特徴量から復元音声を生成する合成モジュール,復元音声に録音機器由来の乗算歪みを付与するチャネルモジュールからなり,入力波形と出力波形の再構成誤差を最小化することで,劣化音声のみから音声復元モデルを学習できる.さらに,提案手法は,劣化音声の生成過程をモデル化することでソース・フィルタ・チャネル成分を別々に抽出できる.実験的評価では,提案手法による音声復元性能および劣化音声特徴の操作性を評価し,提案手法の有効性を示す.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告音声言語情報処理(SLP)"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2022-02-22","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"41","bibliographicVolumeNumber":"2022-SLP-140"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":216642,"updated":"2025-01-19T15:47:06.153338+00:00","links":{},"created":"2025-01-19T01:17:09.885664+00:00"}