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 |
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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: | |
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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