Advances in Speech Recognition by Noam Shabtai

By Noam Shabtai

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By Noam Shabtai

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The overall state of a reservoir very much depends on the connectivity of input neurons with the reservoir. For partially connected inputs, it is less likely that most of the neurons in the reservoir will have short term memory, however, for fully connected inputs, the probability of neurons having the short term memory increases many folds. A series of experiments carried out with different input connectivity and results are reported. In Fig. 18, an architecture is shown with a reservoir size of 8 neurons where input is connected with the reservoir with just one connection.

Processing all frames in the reservoir are computationally expensive therefore specific frames are selected in linear distance with reference to the start point and the end point of the simulations. Each frame consists of the total number of neurons in the reservoir sampled at the rate of 25 ms. Readout Matlab RP Matlab LM Matlab BP Reservoir size Network structure 8 8 8 32-20-10 32-30-10 32-50-10 Table 3. 8 Table 4. Test performance with reservoir size = 15 Readout Reservoir size Network structure 27 27 27 108-20-10 108-30-10 108-50-10 Matlab RP Matlab LM Matlab BP Test accuracy (%) 100 96 100 Table 5.

5. The effect of reverberation on the feature vectors in GMM For reverberant speech, if the RT is larger than the short time Fourier transform (STFT) frame size, there will be time-smearing of the feature vectors. An increase in RT increases this time-smearing. This effect may cause the Gaussian means of the GMM to come closer together. In order to examine this, the weighted average distance between the Gaussians in the GMM and the overall mean feature vector can be calculated in the following form: 42 Advances in Speech Recognition M D = ∑ ωi ( μi − μ )T ( μi − μ ), (19) i =1 where M is the GMM order, ωi is the weight of the i’th Gaussian, μi is the mean vector of the i’th Gaussian, and μ is the overall mean feature vector.

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