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

Li, FF ORCID: https://orcid.org/0000-0001-9053-963X 2005, Estimation of intelligibility from received arbitrary speech signals with support vector machine , in: 2005 International Conference on Machine Learning and Cybernetics, 18th-21st August 2005, Guangzhou, China.

PDF - Published Version Restricted to Repository staff only Download (290kB) | Request a copy

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:	Conference or Workshop Item (Paper)
Schools:	Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title:	2005 International Conference on Machine Learning and Cybernetics
Publisher:	IEEE
ISBN:	0780390911
ISSN:	2160-133X
Related URLs:	Publication
Funders:	MMU
Depositing User:	Dr Francis F. Li
Date Deposited:	11 May 2016 14:22
Last Modified:	15 Feb 2022 20:47
URI:	http://usir.salford.ac.uk/id/eprint/38944

Actions (login required)

Edit record (repository staff only)