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Nonintrusive Quality Assessment of Noise Suppressed Speech With Mel-Filtered Energies and Support Vector Regression

Narwaria, Manish, Lin, Weisi, McLoughlin, Ian Vince, Emmanuel, Sabu, Chia, Liang-Tien (2012) Nonintrusive Quality Assessment of Noise Suppressed Speech With Mel-Filtered Energies and Support Vector Regression. Audio, Speech, and Language Processing, IEEE Transactions on, 20 (4). pp. 1217-1232. ISSN 1558-7916. (doi:10.1109/TASL.2011.2174223) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:48885)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1109/TASL.2011.2174223

Abstract

Objective 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.

Item Type: Article
DOI/Identification number: 10.1109/TASL.2011.2174223
Subjects: T Technology
Divisions: Faculties > Sciences > School of Computing
Depositing User: Ian McLoughlin
Date Deposited: 25 Aug 2015 10:21 UTC
Last Modified: 29 May 2019 14:40 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/48885 (The current URI for this page, for reference purposes)
McLoughlin, Ian Vince: https://orcid.org/0000-0001-7111-2008
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