By Noam Shabtai

ISBN-10: 9533070978

ISBN-13: 9789533070971

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**Extra resources for Advances in Speech Recognition **

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Numbers in the legend indicate speaker index in NIST-SRE database. (a) 10 Gaussians, male speakers; (b) 50 Gaussians, male speakers; (c) 10 Gaussians, female speakers; and (d) 50 Gaussians, female speakers. 10 1 The Effect of Reverberation on Optimal GMM Order and CMS Performance in Speaker Verification Systems 43 was used, where DRT=0 indicates the weighted average distance between the Gaussians and the overall mean feature vector in the case of clean (non-reverberant) speech. The feature space of the GMMs in Fig.

Fig. 21. This figure shows a response of reservoir with an improved neuron activity. 26 Advances in Speech Recognition Fig. 22. This figure shows a fully connected network where input neuron is fully connected with all the neurons in the reservoir. Fig. 23. This figure shows that almost all the neurons are active in the reservoir and the reservoir dynamics are ordered and correspond to the input stimulus. Neuro-Inspired Speech Recognition Based on Reservoir Computing 27 trivial to model a stable reservoir and very much depends on the experience with this paradigm.

CT−1. , c tn = c tn σn t = 0…T − 1 n = 1… N (8) where for every n = 1 . N, σn is the sample STD of the series cn0 . . cTn − 1 . 4. , 1996]. Speaker verification is the task of accepting or rejecting a tested speaker as a hypothetical speaker. Let X = [x0, x1, . . , xT−1] (9) be a segment of speech feature vectors xt of discrete time t ∈ {0, 1, . . , T − 1}. Let H1 represent the event that the tested speaker is the hypothetical speaker, and let H0 represent the opposite event. The model λ1 is defined to contain the parameters such that a parametric probability density function (PDF) p(X; λ1) would model the conditional PDF p(X|H1).

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