{"updated":"2025-01-21T09:03:52.316573+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00107375","sets":["1164:5159:7427:7760"]},"path":["7760"],"owner":"11","recid":"107375","title":["調音クラスの事後確率に基づく言語識別の検討"],"pubdate":{"attribute_name":"公開日","attribute_value":"2014-12-08"},"_buckets":{"deposit":"b0db66fa-188d-4d39-bcfa-9c10948f9fe8"},"_deposit":{"id":"107375","pid":{"type":"depid","value":"107375","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"調音クラスの事後確率に基づく言語識別の検討","author_link":["16935","16932","16933","16934"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"調音クラスの事後確率に基づく言語識別の検討"},{"subitem_title":"Automatic Language Identification Based on Posterior Probability on Articulatory Classes","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"ポスター・デモセッション","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2014-12-08","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"電気通信大学"},{"subitem_text_value":"電気通信大学"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"The University of Electro-Communications","subitem_text_language":"en"},{"subitem_text_value":"The University of Electro-Communications","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/107375/files/IPSJ-SLP14104028.pdf"},"date":[{"dateType":"Available","dateValue":"2100-01-01"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-SLP14104028.pdf","filesize":[{"value":"433.1 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"22"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"e39764f2-7bd9-4d7f-89b0-42158d219526","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2014 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"平田, 拓海"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"高木, 一幸"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Takumi, Hirata","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kazuyuki, Takagi","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":"言語識別とは，入力音声に対しその言語が何語であるかを自動的に判別する技術である．言語識別では言語を区別する特徴の抽出が重要である．本研究では調音特徴に基づく調音クラスの事後確率を言語識別に用いる．音声のスペクトル特徴に対する各調音クラスの事後確率を GMM を用いて求め，これらを束ねた事後確率ベクトルの時系列をベクトル量子化し，VQ 符号時系列を得る．言語毎の VQ 符号時系列の n-gram を言語識別用のモデルとする．識別時には，n-gram モデルの入力音声の調音クラス事後確率の VQ 符号時系列に対する尤度が最も高い言語を識別結果とする；提案手法を用いた日英 2 言語識別実験では 97.1％の識別率を得た．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Extraction of features from input speech that are effective in distinguishing the language is a key issue for language identification system. We use posterior probabilities on articulatory classes as features for language identification. Posterior probability on each articulatory class is calculated by GMMs. Each GMM is trained with MFCC data of speech segments labeled with the phonemes or acoustic events that correspond to the articulatory class. The posterior probability values of the articulatory classes are concatenated to form an articulatory-feature- class-posterior-probability (AFCPP) vector at each analysis frame. These vectors are then quantized to yield VQ code sequence, which is used as the training data for a n-gram language model. Language identification is performed by selecting the n-gram model that yields the highest likelihood for the AFCPP vector sequence of the input utterance. Language identification experiment between Japanese and English by the present method showed identification rate of 97.1%.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"5","bibliographic_titles":[{"bibliographic_title":"研究報告音声言語情報処理（SLP）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2014-12-08","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"28","bibliographicVolumeNumber":"2014-SLP-104"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"created":"2025-01-18T23:50:34.106529+00:00","id":107375,"links":{}}