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CONSTRUCTION METHOD FOR DEEP LONG SHORT-TERM MEMORY RECURRENT NEURAL NETWORK ACOUSTIC MODEL BASED ON SELECTIVE ATTENTION PRINCIPLE
CONSTRUCTION METHOD FOR DEEP LONG SHORT-TERM MEMORY RECURRENT NEURAL NETWORK ACOUSTIC MODEL BASED ON SELECTIVE ATTENTION PRINCIPLE
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机译:基于选择性注意原则的深层短期记忆递归神经网络声学模型的构建方法
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摘要
A construction method for a deep long short-term memory recurrent neural network acoustic model based on a selective attention principle. Change of an instant function of neurons of an auditory cortex is represented by adding an attention gate (103) unit in the deep long short-term memory recurrent neural network acoustic model, and the attention gate (103) unit is different from other gate units in that: the other gate units correspond to a time sequence on a one-to-one basis, but the attention gate (103) unit shows a short-term plasticity effect, thereby having intervals on the time sequence; extraction of robust features about Cross-talk noise and construction of a robust acoustic model are realized via the recurrent neural network acoustic model obtained by training a large amount of voice data containing the Cross-talk noise, and the purpose of increasing the robustness about the acoustic model can be achieved by restraining the influence of a non-target stream against the extraction of the features; the method can be extensively applied to the field of a plurality of machine learning related to speaker recognition and keyword recognition in voice recognition, human-machine interaction and the like.
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