Enhancing Single Speaker Recognition Using Deep Belief Network

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Recognition in speech is complex phenomena study, and the reason for this is the complexity of human language. The barrier of the problem in speech recognition study now can be handled from speech signal using machine learning methods. Nowadays, Deep Belief Networks (DBN) automatically is able to find out the representation of speech signal. This paper tries to approach a structure optimization of DBN which based on the combined technique of evolutionary computation to enhance the single speaker speech. It firstly extracts from the feature of speech signal then applies them to construct lots of random subspaces. The result of the conducted experimental in the evolutionary computation of DBN indicates the structure have an improvement for speech recognition.

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