Advances in Speech Recognition by Noam Shabtai

By Noam Shabtai

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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.

4 Body-Conducted Speech Recognition and its Application to Speech Support System Shunsuke Ishimitsu Hiroshima City University Japan 1. Introduction In recent years, speech recognition systems have been used in a wide variety of environments, including internal automobile systems. Speech recognition plays a major role in a dialoguetype marine engine operation support system currently under investigation. In this system, speech recognition would come from the engine room, which contains the engine apparatus, electric generator, and other equipment.

For this experiment, the total dataset consisted of 200 samples, divided into two sets (training and testing), 20 samples (5 speakers x 4 utterances) for each digit with 28 LPC features per sample. In order to analyse the classification accuracy, different training sets and hidden layer neurons were investigated. The results in terms of classification accuracy are shown in Tabs. 1 and 2. In a series of experiments, the best results obtained in those trials are shown in the following tables. 01.

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