Research On Speech Recognition Based On Spiking Neural Networks

Recently, Spiking Neural Networks are considered as a new computation paradigm, representing the next generation of Artificial Neural Networks by offering more flexibility and degrees of freedom for modeling computational elements. Neurological research has shown that spike neurons encode information in the timing of single spikes, and not in their average firing frequency. So the encoding type of SNNs is temporal, and SNN models fits to the analysis of time-structured data very much. Spiking neural networks have been shown to have more powerful computation capability than their non-spiking predecessor as their can use less neurons to solve the same problem. But as far as we know, most researches have been restricted to theoretical work, and there are few applications of SNNs to real-life data. Therefore, we combine the advantage of SNNs to solve temporal problem with the research hotspot of the academia to do some research on Speech Recognition based on spiking neural networks.First, this article gives an introduction about the SNNs, including the model of the spike neuron, spikes coding, neuronal dynamics, Hodgkin-Huxley Model, Network architecture and Applications using SNNs. Also, it gives a comprehensive introduction about speech recognition technology, and has analyzed the problem which the speech recognition is faced with. Clearly, it proposes the approach and the algorithm to do speech recognition based on SNNs, using the H-H equations to simulate the spike neuron and using the circle-map to transform the pulses list to symbol list and computing. At last, the recognition system has been implemented in Matlab, and parameters of H-H equation have been adjusted to improve the results of therecognition.