Abstract
In this paper we propose a system which combines the use of predictive neural networks and the statistical approach in the task of text-independent speaker verification through the telephone line.
The system is composed by a predictive neural network for every reference speaker, which is trained with the back-propagation algorithm and the maximum likelihood criterion, in order to obtain the highest probability when the input to the network belongs to the reference speaker.
We also consider a global network trained on the whole training set whose likelihood gives a measure of the predictability of a given input with the aim to eliminate the strong dependence of the score from the particular input considered.
In order to improve the performances of the proposed system we consider a three states ergodic model for each speaker, in this way we take into account of the non-stationarity of the speech signal.
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References
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© 1997 Springer-Verlag Berlin Heidelberg
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Paoloni, A., Ragazzini, S., Ravaioli, G. (1997). Text independent speaker verification using multiple-state predictive neural networks. In: Bigün, J., Chollet, G., Borgefors, G. (eds) Audio- and Video-based Biometric Person Authentication. AVBPA 1997. Lecture Notes in Computer Science, vol 1206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0016006
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DOI: https://doi.org/10.1007/BFb0016006
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