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By Noam Shabtai
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Extra info for Advances in Speech Recognition
The SVR system was using 20 msec speech frames in which MFCC and ΔMFCC were calculated to form 24-dimensional feature vectors, for which CMS was either applied or not. , 2000]. A background GMM (BGM) of 1024 Gaussians was generated from one-minute long non-reverberant speech segments of 50 speakers, taken from the NIST- 1998 SRE database. This BGM was used to train target AGMMs for 198 male speakers, with one-minute long non-reverberant speech segments, taken from the NIST-1999 SRE [Martin and Przybocki, 2000] database.
Fig. 17 shows the architecture used for these simulations with 27 LIF neurons fully connected with two input neurons. The inputs to the reservoir are two sine waves with different frequencies. Fig. 16. This figure shows spike times in the bottom plot in response to two different inputs (top plot). The vertical blue lines show the neurons firing times in response to input 2 which is clearly separable from input 1. Neuro-Inspired Speech Recognition Based on Reservoir Computing Fig. 17. This figure shows the membrane voltages and spike times in response to two different inputs.
The best accuracy achieved with Poisson encoding was limited to 98%. Fig. 26. Spike times with Poisson encoding for digit1, 2, 4, 6, 7, and 9 Fig. 27. 4 Table 6. 5 Table 7. 3 Table 8. Test performance with reservoir size 15 In this experiment, Poisson spike trains were used as input for reservoir but no significant improvement is achieved by increasing the reservoir size and accuracy found to be inferior than previous experiments where analog values were used. The possible reason is due to the highly random Poisson process, the spike trains were randomly generated and reservoir couldn’t differentiate between different spike trains.