{"id":159895,"date":"2008-01-01T00:00:00","date_gmt":"2008-01-01T00:00:00","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/msr-research-item\/a-speech-enhancement-approach-using-piecewise-linear-approximation-of-an-explicit-model-of-environmental-distortions\/"},"modified":"2018-10-16T20:00:41","modified_gmt":"2018-10-17T03:00:41","slug":"a-speech-enhancement-approach-using-piecewise-linear-approximation-of-an-explicit-model-of-environmental-distortions","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/a-speech-enhancement-approach-using-piecewise-linear-approximation-of-an-explicit-model-of-environmental-distortions\/","title":{"rendered":"A Speech Enhancement Approach using Piecewise Linear Approximation of An Explicit Model of Environmental Distortions"},"content":{"rendered":"<div class=\"asset-content\">\n<p>This paper presents a speech enhancement approach derived by using a piecewise linear approximation (PLA) of an explicit model of environmental distortions. PLA is a generalization of two traditional approaches, namely vector Taylor series (VTS) and MAX approximations. Formulations are described for both maximum likelihood (ML) estimation of noise model parameters and minimum mean-squared error (MMSE) estimation of clean speech. Evaluation experiments are conducted to enhance speech signals corrupted by several types of additive noises. Compared to the traditional MAX-approximation based approach, our PLA-based speech enhancement approach achieves better performance in terms of two objective quality measures, namely segmental SNR and log-spectral distortion.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper presents a speech enhancement approach derived by using a piecewise linear approximation (PLA) of an explicit model of environmental distortions. PLA is a generalization of two traditional approaches, namely vector Taylor series (VTS) and MAX approximations. Formulations are described for both maximum likelihood (ML) estimation of noise model parameters and minimum mean-squared error [&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":"jundu"},{"type":"user_nicename","value":"qianghuo"}],"msr_publishername":"International Speech Communication Association","msr_publisher_other":"","msr_booktitle":"Proc. of INTERSPEECH 2008","msr_chapter":"","msr_edition":"Proc. of INTERSPEECH 2008","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":"\u00a9 2007 ISCA. 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 to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the ISCA and\/or the author.","msr_conference_name":"Proc. of INTERSPEECH 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