Global Soft Decision Employing Support Vector Machine For Speech Enhancement

Title
Global Soft Decision Employing Support Vector Machine For Speech Enhancement
Authors
Chang, J.H.; Jo, Q.H.; Kim, D.K.; Kim, N.S.
Keywords
likelihood ratio, support vector machine
Issue Date
2009-01
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Abstract
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.
URI
http://dspace.inha.ac.kr/handle/10505/1831
ISSN
1070-9908
Appears in Collections:
College of Engineering(공과대학) > Electronic Engineering (전자공학) > Journal Papers, Reports(전자공학 논문, 보고서)
Files in This Item:
Global.pdfDownload

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse