Construction Method of Acoustic Model of Deep Long Short-Term Memory Recurrent Neural Network Based on the Principle of Selective Attention

Abstract

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.

Description

technical field [0001] The invention belongs to the field of audio technology, in particular to a method for constructing an acoustic model of a deep long-term short-term memory cyclic neural network based on the principle of selective attention. Background technique [0002] With the rapid development of information technology, speech recognition technology has the conditions for large-scale commercialization. At present, speech recognition mainly uses continuous speech recognition technology based on statistical models, and its main goal is to find the word sequence with the highest probability represented by a given speech sequence. The task of the continuous speech recognition system based on the statistical model is to find the most probable word sequence represented by the given speech sequence, usually including the construction of acoustic models and language models and their corresponding search and decoding methods. With the rapid development of acoustic models an...

AI Technical Summary

Problems solved by technology

However, most recognition systems are still very sensitive to changes in the acoustic environment, especially under the interference of cross-talk noise (two or more people talking at the same time) and cannot meet the requirements of practical performance.

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