{"links":{},"id":69903,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00069903","sets":["1164:5159:6009:6133"]},"path":["6133"],"owner":"10","recid":"69903","title":["頑健な音声認識のためのウエーブレットパラメータの最適化に基づく残響抑圧"],"pubdate":{"attribute_name":"公開日","attribute_value":"2010-07-15"},"_buckets":{"deposit":"01b74034-0cb8-41ab-917b-1c1bc3037a3c"},"_deposit":{"id":"69903","pid":{"type":"depid","value":"69903","revision_id":0},"owners":[10],"status":"published","created_by":10},"item_title":"頑健な音声認識のためのウエーブレットパラメータの最適化に基づく残響抑圧","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"頑健な音声認識のためのウエーブレットパラメータの最適化に基づく残響抑圧"},{"subitem_title":"Robust Speech Recognition using Optimized Wavelet-based Dereverberation","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"頑健な音声認識","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2010-07-15","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"京都大学学術情報メディアセンター"},{"subitem_text_value":"京都大学学術情報メディアセンター"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"ACCMS, Kyoto University","subitem_text_language":"en"},{"subitem_text_value":"ACCMS, Kyoto University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"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/69903/files/IPSJ-SLP10082005.pdf"},"date":[{"dateType":"Available","dateValue":"2012-07-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-SLP10082005.pdf","filesize":[{"value":"176.1 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":"6ffb8ef6-5931-4c85-9bfa-fe09b6905e9b","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2010 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"ゴメス・ランディ"},{"creatorName":"河原, 達也"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Randy, Gomez","creatorNameLang":"en"},{"creatorName":"Tatsuya, Kawahara","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":"本稿では、音声認識のためのウエーブレットに基づく残響抑圧法について述べる。残響抑圧は、遅い残響成分の影響を抑圧するように、ウエーブレット係数をウイナーゲインでフィルタリングすることで行なう。音響モデルの尤度に基づいてウエーブレットのパラメータを最適化することで、元音声と遅い残響成分をそれぞれ効果的に推定することができ、これにより、音声認識に適した残響抑圧のためのウイナーゲインを求めることができる。このウイナーゲイン自体も音響モデルの尤度を用いて調整することで、さらに残響抑圧が改善される。残響のある実データを用いた評価実験により、有意な音声認識精度の改善を得ることができた。","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"This paper presents an improved wavelet-based dereverberation method for automatic speech recognition (ASR). Dereverberation is based on filtering reverberant wavelet coefficients with the Wiener gains to suppress the effect of the late reflections. Optimization of the wavelet parameters using acoustic model enables the system to estimate the clean speech and late reflections effectively. This results to a better estimate of the Wiener gains for dereverberation in the ASR application. Additional tuning of the parameters of the Wiener gain in relation with the acoustic model further improves the dereverberation process for ASR. In the experiment with real reverberant data, we have achieved a significant improvement in ASR accuracy.","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":"2010-07-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"5","bibliographicVolumeNumber":"2010-SLP-82"}]},"relation_version_is_last":true,"weko_creator_id":"10"},"created":"2025-01-18T23:29:15.078079+00:00","updated":"2025-01-21T23:42:46.863963+00:00"}