An analysis is made of the sensitivity of feedforward layered networks of Adaline elements (threshold logic units) to weight errors. An approximation is derived which expresses the probability of error for an output neuron of a large network (a network with many neurons per layer) as a function of the percentage change in the weights. As would be expected, the probability of error increases with the number of layers in the network and with the percentage change in the weights. The probability of error is essentially independent of the number of weights per neuron and of the number of neurons per layer, as long as these numbers are large (on the order of 100 or more).
The task of a playback attack detector (PAD) is to decide whether an incoming recording shares the same originating utterance as any of N stored recordings. All recordings are noisy channel-distorted versions of the same phrase uttered by the same person; the originating utterances of the N stored recordings are assumed to be distinct. The proposed approach makes a decision based on a set of N similarity scores which quantify the similarity between the incoming recording and each of the N stored recordings. Although satisfactory results are obtained by thresholding the maximum of the N scores using speaker and phrase (SaP)-dependent thresholds, it is shown that the use of a relative similarity score (a normalized version of the maximum similarity score) results in significant performance improvements especially in the case when the incoming recording is a severely distorted version of a stored recording utterance, as well as for the case when SaP-independent thresholds are used.
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