dc.contributor.authorNarwaria, Manish
dc.contributor.authorLin, Weisi
dc.contributor.authorMcLoughlin, Ian Vince
dc.contributor.authorEmmanuel, Sabu
dc.contributor.authorChia, Clement Liang-Tien
dc.identifier.citationNarwaria, M., Lin, W., McLoughlin, I. V., Emmanuel, S., & Chia, C. L. T. (2012). Nonintrusive quality assessment of noise suppressed speech with Mel-Filtered energies and support vector regression. IEEE transactions on audio, speech, and language processing, 20(4), 1217-1232.
dc.description.abstractObjective speech quality assessment is a challenging task which aims to emulate human judgment in the complex and time consuming task of subjective assessment. It is difficult to perform in line with the human perception due the complex and nonlinear nature of the human auditory system. The challenge lies in representing speech signals using appropriate features and subsequently mapping these features into a quality score. This paper proposes a nonintrusive metric for the quality assessment of noise-suppressed speech. The originality of the proposed approach lies primarily in the use of Mel filter bank energies (FBEs) as features and the use of support vector regression (SVR) for feature mapping. We utilize the sensitivity of FBEs to noise in order to obtain an effective representation of speech towards quality assessment. In addition, the use of SVR exploits the advantages of kernels which allow the regression algorithm to learn complex data patterns via nonlinear transformation for an effective and generalized mapping of features into the quality score. Extensive experiments conducted using two third party databases with different noise-suppressed speech signals show the effectiveness of the proposed approach.en_US
dc.relation.ispartofseriesIEEE transactions on audio, speech, and language processingen_US
dc.titleNonintrusive quality assessment of noise suppressed speech with Mel-Filtered energies and support vector regressionen_US
dc.typeJournal Article
dc.contributor.schoolSchool of Computer Engineeringen_US

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