Estimation of unknown speaker’s height from speech

Authors: Iosif Mporas Todor Ganchev

Publish Date: 2010/01/28

Volume: 12, Issue: 4, Pages: 149-160

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Abstract

In the present study we propose a regressionbased 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 nonlinear 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 0053 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

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