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Erschienen in:
2007 | OriginalPaper | Buchkapitel
Text-Independent Speaker Authentication with Spiking Neural Networks
verfasst von : Simei Gomes Wysoski, Lubica Benuskova, Nikola Kasabov
Erschienen in: Artificial Neural Networks – ICANN 2007
Verlag: Springer Berlin Heidelberg
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This paper presents a novel system that performs text-independent speaker authentication using new spiking neural network (SNN) architectures. Each speaker is represented by a set of prototype vectors that is trained with standard Hebbian rule and
winner-takes-all
approach. For every speaker there is a separated spiking network that computes normalized similarity scores of MFCC (Mel Frequency Cepstrum Coefficients) features considering speaker and background models. Experiments with the VidTimit dataset show similar performance of the system when compared with a benchmark method based on vector quantization. As the main property, the system enables optimization in terms of performance, speed and energy efficiency. A procedure to create/merge neurons is also presented, which enables adaptive and on-line training in an evolvable way.