Speaker recognition is a task of remarkable relevance, with applications in diversified domains. Recently, mainly due to the facilities in audio-visual content acquisition, the capacity of analyzing growing datasets ...

This paper presents a framework for maximum a posteriori (MAP) speaker adaptation of state duration distributions in hidden Markov models (HMM). Four key issues of MAP estimation, namely analysis and modeling of state ...

Probabilistic linear discriminant analysis (PLDA) is the leading method for computing scores in speaker recognition systems. The method models the vectors representing each audio sample as a sum of three terms: one that ...

The effect of recognizing a person by his voice, through a machine, is known as automatic speaker recognition. This technique sets up a complex problem when considering algorithms that demand a fast processing and the ...

In this paper, an unsupervised intra-speaker variability compensation (ISVC) method based oil Gestalt is proposed to address the problem of limited enrolling data and noise robustness in text-dependent speaker verification ...

In this paper, an unsupervised intra-speaker variability compensation (ISVC) method based oil Gestalt is proposed to address the problem of limited enrolling data and noise robustness in text-dependent speaker verification ...

This paper deals with the problematic of speaker variability in a task of pronunciation verification for the speech therapy of children and young adults in Computer-Aided Pronunciation Training (CAPT) tools. The baseline ...

The fastest and most straightforward way of communication for mankind is the voice. Therefore, the best way to interact with computers should be the voice too. That is why at the moment men are searching new ways to interact ...

Probabilistic linear discriminant analysis (PLDA) is a method used for biometric problems like speaker or face recognition that models the variability of the samples using two latent variables, one that depends on the class ...