{"id":162719,"date":"2004-01-01T00:00:00","date_gmt":"2004-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/dimensionality-reduction-using-mce-optimized-lda-transformation\/"},"modified":"2018-10-16T20:51:14","modified_gmt":"2018-10-17T03:51:14","slug":"dimensionality-reduction-using-mce-optimized-lda-transformation","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/dimensionality-reduction-using-mce-optimized-lda-transformation\/","title":{"rendered":"Dimensionality reduction using MCE-optimized LDA transformation"},"content":{"rendered":"<div class=\"asset-content\">\n<p>In this paper, Minimum Classification Error (MCE) method is<br \/>\nextended to optimize both Linear Discriminant Analysis (LDA)<br \/>\ntransformation and the classification parameters for<br \/>\ndimensionality reduction. Firstly, under the HMM-based<br \/>\nContinuous Speech Recognition (CSR) framework, we use<br \/>\nMCE criterion to optimize the conventional dimensionality<br \/>\nreduction method, which uses LDA to transform standard<br \/>\nMFCCs. Then, a new dimensionality reduction method is<br \/>\nproposed. In the new method, the combination of Discrete<br \/>\nCosine Transform (DCT) and LDA, as used in the conventional<br \/>\nmethod, is replaced by a single LDA transformation, which is<br \/>\noptimized according to MCE criterion along with the<br \/>\nclassification parameters. Experimental results on TiDigits<br \/>\nshow that even when the feature dimension is reduced to 14, the<br \/>\nperformance of this new method is as good as that of the MCEtrained<br \/>\nsystem using 39 dimension MFCCs. It also outperforms<br \/>\nour MCE-optimized conventional dimensionality reduction<br \/>\nmethod.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper, Minimum Classification Error (MCE) method is extended to optimize both Linear Discriminant Analysis (LDA) transformation and the classification parameters for dimensionality reduction. Firstly, under the HMM-based Continuous Speech Recognition (CSR) framework, we use MCE criterion to optimize the conventional dimensionality reduction method, which uses LDA to transform standard MFCCs. Then, a new [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"jinyli"}],"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Proc. ICASSP","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"Proc. 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