{"id":156683,"date":"2003-04-01T00:00:00","date_gmt":"2003-04-01T00:00:00","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/msr-research-item\/an-expectation-maximization-approach-for-formant-tracking-using-a-parameter-free-nonlinear-predictor\/"},"modified":"2018-10-16T21:03:52","modified_gmt":"2018-10-17T04:03:52","slug":"an-expectation-maximization-approach-for-formant-tracking-using-a-parameter-free-nonlinear-predictor","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/an-expectation-maximization-approach-for-formant-tracking-using-a-parameter-free-nonlinear-predictor\/","title":{"rendered":"An Expectation-Maximization Approach for Formant Tracking using a Parameter-free Nonlinear Predictor"},"content":{"rendered":"<p>This paper presents a new approach for formant tracking using a parameter-free non-linear predictor that maps formant frequencies and bandwidths into the acoustic feature space. The approach relies on decomposing the speech signal into two components: the \ufb01rst component captures the mapping between formants and acoustic observations, while the second componentis intended to capture the residual in the signal. We build the mapping by quantizing the formant space and creating a predictor codebook. Formant tracking is achieved by: 1) EM training of the parameters of the residual component, and 2) searching the predictor codebook for the best formant values. We explore both MAP and MMSE methods for performing formant tracking with the proposed approach. Furthermore, we impose \ufb01rst order continuity constraints on formant trajectories, and use Viterbi search to perform formant tracking. We present formant tracking results on data from the Switchboard corpus.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper presents a new approach for formant tracking using a parameter-free non-linear predictor that maps formant frequencies and bandwidths into the acoustic feature space. The approach relies on decomposing the speech signal into two components: the \ufb01rst component captures the mapping between formants and acoustic observations, while the second componentis intended to capture the [&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":null,"msr_publishername":"Institute of Electrical and Electronics Engineers, Inc.","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Proc. of the IEEE International Conference on Acoustics, Speech, and Signal 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":"\u00a9 2007 IEEE. 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 IEEE.","msr_conference_name":"Proc. of the IEEE International Conference on Acoustics, Speech, and Signal 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