Vasileios Vasilakakis

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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)
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 is deferred 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)
This paper describes the experimental setup and the results obtained using several state-of-the-art speaker recognition classifiers. The comparison of the different approaches aims at the development of real world applications, taking into account memory and computational constraints, and possible mismatches with respect to the training environment. The(More)
This paper focuses on the extraction of i-vectors, a compact representation of spoken utterances that is used by most of the state–of–the–art speaker recognition systems. This work was mainly motivated by the need of reducing the memory demand of the huge data structures that are usually precomputed for fast computation of the i-vectors. We propose a set of(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)
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