Vasileios Vasilakakis

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1. Abstract Most state–of–the–art speaker recognition systems are based on Gaussian Mixture Models (GMMs), where a speech segment is represented by a compact representation, referred to as " identity vector " (ivector for short), extracted by means of Factor Analysis. The main advantage of this representation is that the problem of intersession variability(More)
This work presents a new and efficient approach to discriminative speaker verification in the i-vector space. We illustrate the development of a linear discriminative classifier that is trained to discriminate between the hypothesis that a pair of feature vectors in a trial belong to the same speaker or to different speakers. This approach is alternative to(More)
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(More)
Porto, the institutional repository of the Politecnico di Torino, is provided by the University Library and the IT-Services. The aim is to enable open access to all the world. Please share with us how this access benefits you. Your story matters. Abstract This paper focuses on the extraction of i-vectors, a compact representation of spoken utterances that(More)
Porto, the institutional repository of the Politecnico di Torino, is provided by the University Library and the IT-Services. The aim is to enable open access to all the world. Please share with us how this access benefits you. Your story matters. Publisher copyright claim: c 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be(More)
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