Deep long-term and short-term memory recurrent neural network acoustic model establishing method based on selective attention principles

Abstract

Disclosed is a deep long-term and short-term memory recurrent neural network acoustic model establishing method based on selective attention principles. According to the deep long-term and short-term memory recurrent neural network acoustic model establishing method based on the selective attention principles, attention gate units are added inside a deep long-term and short-term memory recurrent neural network acoustic model to represent instantaneous function change of auditory cortex neurons; the gate units are different in other gate units in that the other gate units are in one-to-one correspondence with time series, while the attention gate units represent short-term plasticity effects and accordingly have intervals in the time series; through the neural network acoustic model obtained by training mass voice data containing Cross-talk noise, robustness feature extraction of the Cross-talk noise and establishment of robust acoustic models can be achieved; the aim of improving the robustness of the acoustic models can be achieve by inhibiting influence of non-target flow on feature extraction. The deep long-term and short-term memory recurrent neural network acoustic model establishing method based on the selective attention principles can be widely applied to multiple voice recognition-related machine learning fields of speaker recognition, keyword recognition, man-machine interaction and the like.

Description

Technical field [0001] The invention belongs to the field of audio technology, and particularly relates to a method for constructing a deep long-short-term memory loop neural network acoustic model based on the selective attention principle. 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 through a given speech sequence. The task of a continuous speech recognition system based on a statistical model is to find the word sequence with the highest probability according to a given speech sequence, which usually includes the construction of acoustic models and language models and their corresponding search and decoding methods. With the rapid development of acoustic models and lan...

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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