Locally Recurrent Probabilistic Neural Networks with Application to Speaker Verification
نویسندگان
چکیده
To improve speaker verification performance, we extend the wellknown Probabilistic Neural Networks (PNN) to Locally Recurrent Probabilistic Neural Networks (LRPNN). In contrast to PNNs that possess no feedbacks, LRPNNs incorporate internal connections to the past outputs of all recurrent neurons, which render them sensitive to the context in which events occur. Thus, LRPNNs are capable of identifying time and spatial correlations. A fast three-step method is proposed for training an LRPNN. The first two steps are identical to the training of traditional PNNs, while the third step is based on the Differential Evolution optimization method. The performance of the proposed LRPNNs is compared with that of the PNNs on the task of text-independent speaker verification.
منابع مشابه
Locally recurrent probabilistic neural network for text-independent speaker verification
This paper introduces Locally Recurrent Probabilistic Neural Networks (LRPNN) as an extension of the well-known Probabilistic Neural Networks (PNN). A LRPNN, in contrast to a PNN, is sensitive to the context in which events occur, and therefore, identification of time or spatial correlations is attainable. Besides the definition of the LRPNN architecture a fast three-step training method is pro...
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