Height estimation from speech signals using i-vectors and least-squares support vector regression

Abstract

This paper proposes a novel approach for automatic speaker height estimation based on the i-vector framework. In this method, each utterance is modeled by its corresponding i-vector. Then artificial neural networks (ANNs) and least-squares support vector regression (LSSVR) are employed to estimate the height of a speaker from a given utterance. The proposed method is trained and tested on the telephone speech signals of National Institute of Standards and Technology (NIST)2008 and 2010 Speaker Recognition Evaluation (SRE) corpora respectively. Evaluation results show the effectiveness of the proposed method in speaker height estimation.

DOI: 10.1109/TSP.2015.7296469

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Cite this paper

@article{Poorjam2015HeightEF, title={Height estimation from speech signals using i-vectors and least-squares support vector regression}, author={Amir Hossein Poorjam and Mohamad Hasan Bahari and Vasileios Vasilakakis and Hugo Van hamme}, journal={2015 38th International Conference on Telecommunications and Signal Processing (TSP)}, year={2015}, pages={1-5} }