Abstract
<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> In this letter, we propose a novel speech enhancement technique based on global soft decision incorporating a support vector machine (SVM). Global soft decision in the proposed approach is performed employing the probabilistic outputs of the SVM rather than the conventional Bayes' rule. Actually, global speech absence probability (GSAP) is determined by the sigmoid function based on key parameters estimated by the model-trust minimization algorithm of the SVM output. Improved results are obtained in terms of speech quality measures for various types of noise and at different signal-to-noise ratio (SNR) levels when the proposed SVM is adopted in the global soft decision for speech enhancement. </para>
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
