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An Investigation on Mutual Information for the Linear Predictive System and the Extrapolation of Speech Signals.
Institute of Communication Acoustics, Ruhr-Universität Bochum.
Institute of Communication Acoustics, Ruhr-Universität Bochum.
KTH, School of Electrical Engineering (EES), Sound and Image Processing.
KTH, School of Electrical Engineering (EES), Sound and Image Processing.
2012 (English)In: Speech Communication; 10. ITG Symposium; Proceedings of, 2012, 1-4 p.Conference paper, Published paper (Refereed)
Abstract [en]

Mutual information (MI) is an important information theoretic concept which has many applications in telecommunications, in blind source separation, and in machine learning. More recently, it has been also employed for the instrumental assessment of speech intelligibility where traditionally correlation based measures are used. In this paper, we address the difference between MI and correlation from the viewpoint of discovering dependencies between variables in the context of speech signals. We perform our investigation by considering the linear predictive approximation and the extrapolation of speech signals as examples. We compare a parametric MI estimation approach based on a Gaussian mixture model (GMM) with the knearest neighbor (KNN) approach which is a well-known non-parametric method available to estimate the MI. We show that the GMM-based MI estimator leads to more consistent results.

Place, publisher, year, edition, pages
2012. 1-4 p.
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-105282OAI: oai:DiVA.org:kth-105282DiVA: diva2:570594
Conference
ITG Conference on Speech Communication
Note

QC 20130524

Available from: 2012-11-20 Created: 2012-11-20 Last updated: 2013-05-24Bibliographically approved

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Output format
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