Estimation of intelligibility from received arbitrary speech signals with support vector machine

Li, FF 2005, 'Estimation of intelligibility from received arbitrary speech signals with support vector machine' , 2005 International Conference on Machine Learning and Cybernetics (Volume:6 ) , 3755-3760 Vol. 6.

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Abstract

Intelligibility, a vital concern of a speech transmission channel, is quantified using speech transmission index (STI). The standard STI method relies on noisy test signals and thus hinders in-use measurements. Alternative methods to accurately estimate the STI from naturally occurring speech signals have been developed over the past few years using artificial neural networks. This paper presents a new machine learning based method to more accurately estimate the STI from arbitrary running speech using a purpose design signal pre-processor and support vector machines. When compared with the neural network approaches to the problem, the new method exhibits improved estimation accuracy and generalisation capability to arbitrary speech, providing a more applicable method to facilitate in-situ measurements.

Item Type:	Article
Schools:	Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre (SIRC)
Journal or Publication Title:	2005 International Conference on Machine Learning and Cybernetics (Volume:6 )
Publisher:	IEEE
ISSN:	2160-133X
Related URLs:	Publication
Funders:	MMU
Depositing User:	FF Li
Date Deposited:	11 May 2016 14:22
Last Modified:	09 Aug 2017 16:28
URI:	http://usir.salford.ac.uk/id/eprint/38944

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