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International Journal of Speech Technology

, Volume 12, Issue 4, pp 149–160 | Cite as

Estimation of unknown speaker’s height from speech

  • Iosif Mporas
  • Todor Ganchev
Article

Abstract

In the present study, we propose a regression-based scheme for the direct estimation of the height of unknown speakers from their speech. In this scheme every speech input is decomposed via the openSMILE audio parameterization to a single feature vector that is fed to a regression model, which provides a direct estimation of the persons’ height. The focus in this study is on the evaluation of the appropriateness of several linear and non-linear regression algorithms on the task of automatic height estimation from speech. The performance of the proposed scheme is evaluated on the TIMIT database, and the experimental results show an accuracy of 0.053 meters, in terms of mean absolute error, for the best performing Bagging regression algorithm. This accuracy corresponds to an averaged relative error of approximately 3%. We deem that the direct estimation of the height of unknown people from speech provides an important additional feature for improving the performance of various surveillance, profiling and access authorization applications.

Keywords

Human height estimation from speech Speech processing Regression algorithms 

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Wire Communications Laboratory, Dept. of Electrical and Computer EngineeringUniversity of PatrasRion-PatrasGreece

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