Optimization method and system of stacked one-dimensional convolutional network wake-up acoustic model

Abstract

The embodiment of the invention provides an optimization method of a stacked one-dimensional convolutional network wake-up acoustic model. The method comprises the following steps: adjusting an expansion coefficient of a time domain convolutional layer in a stacked one-dimensional convolutional network wake-up acoustic model, and increasing a receptive field output by the time domain convolutional layer; and setting an activation function of the time domain convolution layer as a gated linear unit, and combining the gated linear unit with the output of the time domain convolution layer to reduce the dimension of the output of the time domain convolution layer so as to optimize the stacked one-dimensional convolution network wake-up acoustic model. The embodiment of the invention further provides a system for optimizing the stacked one-dimensional convolutional network wake-up acoustic model. According to the embodiment of the invention, the interval of the convolution kernel causes the increase of the receptive field, so that the receptive field of the model is effectively increased, the wake-up precision is improved, meanwhile, after the gating linear unit is combined with the S1DCNN model, the output dimension can be reduced to half of the original dimension, the model parameter quantity is better compressed, and a higher wake-up rate can be achieved under the same parameter quantity.

Description

technical field [0001] The present invention relates to the field of intelligent speech, in particular to a stacked one-dimensional convolutional network wake-up acoustic model optimization method and system. Background technique [0002] S1DCNN (Stacked 1D convolutional networks, stacked one-dimensional convolutional network) awakens the acoustic model, which is composed of several different S1DCNN layers; each S1DCNN layer is mainly composed of two one-dimensional convolutional layers (cnn). The first convolutional layer is frequency-domain convolution, the second convolutional layer is time-domain convolution, and each channel is independent of each other, which is called depth-wise (depth) convolution. Compared with the traditional two-dimensional CNN, S1DCNN can achieve the same level of performance while reducing the amount of calculation, or the performance loss is small, and it has a higher performance in the lightweight acoustic model (for example, the acoustic mode...

AI Technical Summary

Problems solved by technology

[0006] In order to at least solve the problem that the stacked one-dimensional convolutional network wakes up the acoustic model with a relatively single receptive field and low stability

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