{"created":"2022-01-28T01:00:21.936409+00:00","id":2005058,"links":{},"metadata":{"_buckets":{"deposit":"b5efcf5c-9b16-4430-88b5-eb37f94081cf"},"_deposit":{"id":"2005058","owners":[1],"pid":{"revision_id":0,"type":"depid","value":"2005058"},"status":"published"},"_oai":{"id":"oai:u-ryukyu.repo.nii.ac.jp:02005058","sets":["1642838403123","1642838403551:1642838406845"]},"author_link":[],"item_1617186331708":{"attribute_name":"Title","attribute_value_mlt":[{"subitem_1551255647225":"遺伝的アルゴリズムを用いた隠れマルコフモデルによる音声自動認識に関する研究","subitem_1551255648112":"ja"},{"subitem_1551255647225":"Study on Automatic Speech Recognition with the Hidden Markov Model Using the Genetic Algorithm","subitem_1551255648112":"en"}]},"item_1617186419668":{"attribute_name":"Creator","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"高良, 富夫","creatorNameLang":"ja"}]},{"creatorNames":[{"creatorName":"長山, 格","creatorNameLang":"ja"}]},{"creatorNames":[{"creatorName":"Takara, Tomio","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Nagayama, Itaru","creatorNameLang":"en"}]}]},"item_1617186476635":{"attribute_name":"Access Rights","attribute_value_mlt":[{"subitem_1522299639480":"open access","subitem_1600958577026":"http://purl.org/coar/access_right/c_abf2"}]},"item_1617186609386":{"attribute_name":"Subject","attribute_value_mlt":[{"subitem_1522299896455":"ja","subitem_1522300014469":"Other","subitem_1523261968819":"音声自動認識"},{"subitem_1522299896455":"ja","subitem_1522300014469":"Other","subitem_1523261968819":"遺伝的アルゴリズム"},{"subitem_1522299896455":"ja","subitem_1522300014469":"Other","subitem_1523261968819":"マルコフモデル"},{"subitem_1522299896455":"ja","subitem_1522300014469":"Other","subitem_1523261968819":"構造"},{"subitem_1522299896455":"ja","subitem_1522300014469":"Other","subitem_1523261968819":"最適化"},{"subitem_1522299896455":"en","subitem_1522300014469":"Other","subitem_1523261968819":"Speech Recognition"},{"subitem_1522299896455":"en","subitem_1522300014469":"Other","subitem_1523261968819":"Genetic Algorithm"},{"subitem_1522299896455":"en","subitem_1522300014469":"Other","subitem_1523261968819":"Markov Model"},{"subitem_1522299896455":"en","subitem_1522300014469":"Other","subitem_1523261968819":"Strucyure"},{"subitem_1522299896455":"en","subitem_1522300014469":"Other","subitem_1523261968819":"Optimization"}]},"item_1617186626617":{"attribute_name":"Description","attribute_value_mlt":[{"subitem_description":"科研費番号: 09680374","subitem_description_type":"Other"},{"subitem_description":"平成9年度～平成11年度科学研究費補助金(基盤研究(C)(2))研究成果報告書","subitem_description_type":"Other"},{"subitem_description":"研究概要：情報処理システムと人間との間で情報の授受を行うとき、音声言語を媒介とすることは、人間にとって最も根源的かつ高速で便利な手段である。本研究では、情報処理システムが音声言語を受理する機能である音声自動認識の高性能化をめざして、遺伝的アルゴリズムを用いた認識モデルの構成方法を確立し、その有効性を実験的に明らかにすることを目的としている。音声自動認識のためのモデルとしては、音声の生成を確率過程としてとらえる隠れマルコフモデル(HMM)を用いる方法が現在のところ最も有望である。しかし、この方法では、最適なモデルの構造を決定する効果的なアルゴリズムが確立されていない。そこで本研究では、HMMの構造を自動的に決定するため、生物の進化過程をモデル化した遺伝的アルゴリズム(GA)を応用する方法を提案している。この方法では、世代を経るに従い尤度の低いモデルは淘汰され、より認識率の高いモデルが生き残るので、広域的最適な高性能のモデルを得ることができる。まず単語音声認識のための離散型HMMの構造決定にGAを適用した。認識実験の結果、クローズ実験だけでなくオープン実験においても、世代を経るに従い認識率の高い構造が得られることが示された。認識率は、通常よく使われ性能も高いLeft-Right構造よりも高くなり、GAの有効性が示された。次に、連続型HMMにGAを適用し、離散型HMMと同様に有効な結果が得られることを示した。さらに、この方法の改良法として、語彙単語をセットにした符号化、隠れ遺伝子、及び状態単位での交叉・突然変異が有効であることを示した。","subitem_description_type":"Other"},{"subitem_description":"要約（欧文）:Spoken language is the most fundamental, fast and convenient method for human to communicate with information processing systems. An automatic speech recognition is the function of speech perception for the information processing system. The purpose of this research is to develop the model construction method for recognition systems with high performance using the genetic algorithm (GA), and to show the effectiveness of the method experimentally. The hidden Markov models (HMMs) are widely used for automatic speech recognition. However, the HMM has a problem still unresolved, i.e. how to design the optimal structure of the model. In order to search out the optimal structure of the HMM, we propose in this study the application of the GA which is the model of natural evolution process. In this algorithm, models with higher performance survive and models with lower likelihood die as the generation proceeds, then finally, the globally optimal structure is obtained. First, we applied the GA to the determination of the discrete HMM's structure for spoken word recognition. As a result of the recognition experiment, it was shown that the structures with higher recognition scores are obtained as the generation proceeds, not only in the case of closed tests but also open tests. The recognition score became higher than that of the Left-Right structure which is the most popular and with high performance, and the effectiveness of the GA was shown. Next, the GA was applied to the continuous HMM, and the effective result was obtained similarly to the discrete HMM. As the revised version of this method, the coding method of word set, the hidden gene method and the crossover and mutation in a state were shown to be effective.","subitem_description_type":"Other"},{"subitem_description":"未公開：P.5以降(別刷論文のため)","subitem_description_type":"Other"},{"subitem_description":"研究報告書","subitem_description_type":"Other"}]},"item_1617186643794":{"attribute_name":"Publisher","attribute_value_mlt":[{"subitem_1522300295150":"ja","subitem_1522300316516":"高良富夫"}]},"item_1617186702042":{"attribute_name":"Language","attribute_value_mlt":[{"subitem_1551255818386":"jpn"}]},"item_1617186783814":{"attribute_name":"Identifier","attribute_value_mlt":[{"subitem_identifier_type":"HDL","subitem_identifier_uri":"http://hdl.handle.net/20.500.12000/13547"}]},"item_1617186920753":{"attribute_name":"Source Identifier","attribute_value_mlt":[{"subitem_1522646500366":"NCID","subitem_1522646572813":"BA46509145"}]},"item_1617187056579":{"attribute_name":"Bibliographic Information","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2000-04","bibliographicIssueDateType":"Issued"}}]},"item_1617258105262":{"attribute_name":"Resource Type","attribute_value_mlt":[{"resourcetype":"research report","resourceuri":"http://purl.org/coar/resource_type/c_18ws"}]},"item_1617265215918":{"attribute_name":"Version Type","attribute_value_mlt":[{"subitem_1522305645492":"VoR","subitem_1600292170262":"http://purl.org/coar/version/c_970fb48d4fbd8a85"}]},"item_1617605131499":{"attribute_name":"File","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_access","filename":"09680374.pdf","mimetype":"application/pdf","url":{"objectType":"fulltext","url":"https://u-ryukyu.repo.nii.ac.jp/record/2005058/files/09680374.pdf"},"version_id":"e1f945e0-1b33-4a1d-b715-2ba1a998a50e"}]},"item_title":"遺伝的アルゴリズムを用いた隠れマルコフモデルによる音声自動認識に関する研究","item_type_id":"15","owner":"1","path":["1642838403123","1642838406845"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2009-12-01"},"publish_date":"2009-12-01","publish_status":"0","recid":"2005058","relation_version_is_last":true,"title":["遺伝的アルゴリズムを用いた隠れマルコフモデルによる音声自動認識に関する研究"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2022-10-31T02:28:34.439312+00:00"}