{"id":145043,"date":"2002-09-01T00:00:00","date_gmt":"2002-09-01T00:00:00","guid":{"rendered":"https:\/\/newed.any0.dpdns.org\/en-us\/research\/msr-research-item\/log-domain-speech-feature-enhancement-using-sequential-map-noise-estimation-and-a-phase-sensitive-model-of-the-acoustic-environment\/"},"modified":"2018-10-16T20:07:56","modified_gmt":"2018-10-17T03:07:56","slug":"log-domain-speech-feature-enhancement-using-sequential-map-noise-estimation-and-a-phase-sensitive-model-of-the-acoustic-environment","status":"publish","type":"msr-research-item","link":"https:\/\/newed.any0.dpdns.org\/en-us\/research\/publication\/log-domain-speech-feature-enhancement-using-sequential-map-noise-estimation-and-a-phase-sensitive-model-of-the-acoustic-environment\/","title":{"rendered":"Log-Domain Speech Feature Enhancement Using Sequential MAP Noise Estimation and a Phase-sensitive Model of the Acoustic Environment"},"content":{"rendered":"<div class=\"asset-content\">\n<p>In this paper we present an MMSE (minimum mean square error) speech feature enhancement algorithm, capitalizing on a new probabilistic, nonlinear environment model that effectively incorporates the phase relationship between the clean speech and the corrupting noise in acoustic distortion. The MMSE estimator based on this phase-sensitive model is derived and it achieves high efficiency by exploiting single-point Taylor series expansion to approximate the joint probability of clean and noisy speech as a multivariate Gaussian. As an integral component of the enhancement algorithm, we also present a new sequential MAP-based nonstationary noise estimator. Experimental results on the Aurora2 task demonstrate the importance of exploiting the phase relationship in the speech corruption process captured by the MMSE estimator. The phase-sensitive MMSE estimator reported in this paper performs significantly better than phase-insensitive spectral subtraction (54% error rate reduction), and also noticeably better than a phase-insensitive MMSE estimator as our previous state-of-the-art technique reported in [2] (7% error rate reduction), under otherwise identical experimental conditions of speech recognition.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper we present an MMSE (minimum mean square error) speech feature enhancement algorithm, capitalizing on a new probabilistic, nonlinear environment model that effectively incorporates the phase relationship between the clean speech and the corrupting noise in acoustic distortion. The MMSE estimator based on this phase-sensitive model is derived and it achieves high efficiency [&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":"deng"},{"type":"user_nicename","value":"jdroppo"},{"type":"user_nicename","value":"alexac"}],"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"Proc. International Conference on Spoken Language Processing","msr_chapter":"","msr_edition":"Proc. International Conference on Spoken Language Processing","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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