{"_buckets": {"deposit": "02579379-d7c6-43a2-a603-986b18b3ef76"}, "_deposit": {"id": "7728", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "7728"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00007728"}, "item_10_biblio_info_6": {"attribute_name": "\u66f8\u8a8c\u60c5\u5831", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2006", "bibliographicIssueDateType": "Issued"}, "bibliographicPageEnd": "504", "bibliographicPageStart": "501", "bibliographicVolumeNumber": "1", "bibliographic_titles": [{"bibliographic_title": "IEEE International Conference on Acoustics, Speech and Signal Processing"}]}]}, "item_10_description_4": {"attribute_name": "\u6284\u9332", "attribute_value_mlt": [{"subitem_description": "We address issues for improving hands-free speech recognition performance in different car environments using a single distant microphone. In our previous work, we proposed a regression based enhancement method for in-car speech recognition. In this paper, we describe recent improvements and propose a data-driven adaptive regression based speech recognition system, in which both feature enhancement and model compensation are performed. Based on isolated word recognition experiments conducted in 15 real car environments, the proposed adaptive regression approach shows an advantage in average relative word error rate (WER) reductions of 52.5\uff05 and 14.8\uff05, compared to original noisy speech and ETSI advanced front-end, respectively.", "subitem_description_type": "Abstract"}]}, "item_10_identifier_60": {"attribute_name": "URI", "attribute_value_mlt": [{"subitem_identifier_type": "HDL", "subitem_identifier_uri": "http://hdl.handle.net/2237/9447"}]}, "item_10_publisher_32": {"attribute_name": "\u51fa\u7248\u8005", "attribute_value_mlt": [{"subitem_publisher": "IEEE"}]}, "item_10_relation_11": {"attribute_name": "DOI", "attribute_value_mlt": [{"subitem_relation_type_id": {"subitem_relation_type_id_text": "http://dx.doi.org/10.1109/ICASSP.2006.1660067", "subitem_relation_type_select": "DOI"}}]}, "item_10_relation_8": {"attribute_name": "ISBN", "attribute_value_mlt": [{"subitem_relation_type_id": {"subitem_relation_type_id_text": "1-4244-0469-X", "subitem_relation_type_select": "ISBN"}}]}, "item_10_rights_12": {"attribute_name": "\u6a29\u5229", "attribute_value_mlt": [{"subitem_rights": "Copyright \u00a9 2006 IEEE. Reprinted from (relevant publication info). This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Nagoya University\u2019s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org."}]}, "item_10_select_15": {"attribute_name": "\u8457\u8005\u7248\u30d5\u30e9\u30b0", "attribute_value_mlt": [{"subitem_select_item": "publisher"}]}, "item_10_source_id_7": {"attribute_name": "ISSN", "attribute_value_mlt": [{"subitem_source_identifier": "1520-6149", "subitem_source_identifier_type": "ISSN"}]}, "item_10_text_14": {"attribute_name": "\u30d5\u30a9\u30fc\u30de\u30c3\u30c8", "attribute_value_mlt": [{"subitem_text_value": "application/pdf"}]}, "item_creator": {"attribute_name": "\u8457\u8005", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "Li, Weifeng"}], "nameIdentifiers": [{"nameIdentifier": "21911", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Itou, Katunobu"}], "nameIdentifiers": [{"nameIdentifier": "21912", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Takeda, Kazuya"}], "nameIdentifiers": [{"nameIdentifier": "21913", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Itakura, Fumitada"}], "nameIdentifiers": [{"nameIdentifier": "21914", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "\u30d5\u30a1\u30a4\u30eb\u60c5\u5831", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2018-02-19"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "takeda_501.pdf", "filesize": [{"value": "650.1 kB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 650100.0, "url": {"label": "takeda_501.pdf", "url": "https://nagoya.repo.nii.ac.jp/record/7728/files/takeda_501.pdf"}, "version_id": "36ca1024-7a14-463a-8a93-61bb2276c055"}]}, "item_language": {"attribute_name": "\u8a00\u8a9e", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "item_resource_type": {"attribute_name": "\u8cc7\u6e90\u30bf\u30a4\u30d7", "attribute_value_mlt": [{"resourcetype": "journal article", "resourceuri": "http://purl.org/coar/resource_type/c_6501"}]}, "item_title": "ADAPTIVE REGRESSION BASED FRAMEWORK FOR IN-CAR SPEECH RECOGNITION", "item_titles": {"attribute_name": "\u30bf\u30a4\u30c8\u30eb", "attribute_value_mlt": [{"subitem_title": "ADAPTIVE REGRESSION BASED FRAMEWORK FOR IN-CAR SPEECH RECOGNITION"}]}, "item_type_id": "10", "owner": "1", "path": ["320/321/322"], "permalink_uri": "http://hdl.handle.net/2237/9447", "pubdate": {"attribute_name": "\u516c\u958b\u65e5", "attribute_value": "2008-02-22"}, "publish_date": "2008-02-22", "publish_status": "0", "recid": "7728", "relation": {}, "relation_version_is_last": true, "title": ["ADAPTIVE REGRESSION BASED FRAMEWORK FOR IN-CAR SPEECH RECOGNITION"], "weko_shared_id": 3}