{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00057453","sets":["1164:5159:5210:5211"]},"path":["5211"],"owner":"1","recid":"57453","title":["音声の時間変化モデルに基づく音声信号推定法を用いた　非定常雑音下での音声認識"],"pubdate":{"attribute_name":"公開日","attribute_value":"2000-12-21"},"_buckets":{"deposit":"55becb0e-7a73-4b12-b908-e509227cb3d3"},"_deposit":{"id":"57453","pid":{"type":"depid","value":"57453","revision_id":0},"owners":[1],"status":"published","created_by":1},"item_title":"音声の時間変化モデルに基づく音声信号推定法を用いた　非定常雑音下での音声認識","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"音声の時間変化モデルに基づく音声信号推定法を用いた　非定常雑音下での音声認識"},{"subitem_title":"Speech Recognition under Non - stationary Noisy Environments Using Signal Estimation Method Based on Speech State Transition Model","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2000-12-21","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"龍谷大学理工学部"},{"subitem_text_value":"龍谷大学理工学部"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Faculty of Science and Technology, Ryukoku University","subitem_text_language":"en"},{"subitem_text_value":"Faculty of Science and Technology, Ryukoku University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/57453/files/IPSJ-SLP00034004.pdf"},"date":[{"dateType":"Available","dateValue":"2002-12-21"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-SLP00034004.pdf","filesize":[{"value":"565.6 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":"9b5b0b4f-625c-4474-8a71-55d5c31d37f8","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2000 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"藤本, 雅清"},{"creatorName":"有木, 康雉"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Masakiyo, Fujimoto","creatorNameLang":"en"},{"creatorName":"Yasuo, Ariki","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_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"本研究では，音声の時間変化モデルに基づいた非定常雑音に対する雑音除去法を提案する．提案手法では，音声の時間変化モデルをカルマンフィルタによる推定問題に適用することにより，音楽等のような非定常雑音が重畳した音声から，クリーンな音声信号を推定している．音声の時間変化モデルは，雑音重畳音声におけるクリーン音声の時間変動を，Taylor展開を用いることにより表現したモデルである．モデルの構成に必要なパラメータの1つである雑音の変動成分は，線形予測法により推定を行っている．提案手法の評価のために，3種類の音楽が重畳した音声を用いて大語棄連続音声認識を行ない，単語正解精度において，従来法であるParallel Model Combination(PMC)法と比較を行った．その結果，提案手法により，PMC法よりも高い単語正解精度が得られた．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In this paper, we propose a non-stationary noise reduction method based on speech state transition model. Our proposed method estimates the speech signal under non-stationary noisy environments such as musical background by applying speech state transition model to Kalman filtering estimation. The speech state transition model represents the state transition of speech component in non-stationary noisy speech and is modeled by using Taylor expansion. In this model, the state transition of noise component is estimated by using linear predictive estimation. In order to evaluate the proposed method, we carried out large vocabulary continuous speech recognition experiments under 3 types of musics and compared the results with conventionally used Parallel Model Combination(PMC) method in word accuracy rate. As a result, the proposed method obtained word accuracy rate superior to PMC.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"24","bibliographic_titles":[{"bibliographic_title":"情報処理学会研究報告音声言語情報処理（SLP）"}],"bibliographicPageStart":"19","bibliographicIssueDates":{"bibliographicIssueDate":"2000-12-21","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"119(2000-SLP-034)","bibliographicVolumeNumber":"2000"}]},"relation_version_is_last":true,"weko_creator_id":"1"},"id":57453,"updated":"2025-01-22T04:28:46.889876+00:00","links":{},"created":"2025-01-18T23:20:39.208915+00:00"}