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

Li, FF

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.

Citation

Li, F. Estimation of intelligibility from received arbitrary speech signals with support vector machine. Presented at 2005 International Conference on Machine Learning and Cybernetics, Guangzhou, China

• Presentation Conference Type: Other
• Conference Name: 2005 International Conference on Machine Learning and Cybernetics
• Conference Location: Guangzhou, China
• End Date: Aug 21, 2005
• Publication Date: Nov 7, 2005
• Deposit Date: May 11, 2016
• Book Title: 2005 International Conference on Machine Learning and Cybernetics
• ISBN: 0780390911
• DOI: https://doi.org/10.1109/ICMLC.2005.1527593
• Publisher URL: http://dx.doi.org/10.1109/ICMLC.2005.1527593
• Related Public URLs: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10231
• Additional Information: Event Type : Conference; Funders : MMU