Improved time delay neural network acoustic model

Abstract

The invention belongs to the technical field of speech recognition, and relates to an improved time delay neural network acoustic model comprising the steps: a basic TDNN network is established; an attention module is added between two adjacent hidden layers so as to obtain the improved TDNN network; and the improved TDNN network is trained and the final acoustic model is obtained. The attention module is composed of affine transformation and a weighting function. The output of the previous hidden layer is used as input to extract the feature weight value of the input, and the extracted weightvalue is applied to perform weighing of the original input feature so as to obtain the weighted feature. The factors including the modeling capability of the model, the context information extractioncapacity and the model size are considered, and multilayer weighing of the features of the neural network hidden layers is performed to effectively perform explicit modeling of the relative importance of the interlayer features so as to enhance the performance of the TDNN acoustic model and enhance the overall performance of the speech recognition system.

Description

technical field [0001] The invention belongs to the technical field of speech recognition and relates to a delay neural network acoustic model. Background technique [0002] From the birth of the world's first speech recognition system in the 1950s to the first decade of the 21st century, the core technology of speech recognition has undergone a gradual evolution from template matching to statistical model building. The most classic and still significant in the field of speech recognition is the method of combining Hidden Markov Model (HMM) and Gaussian Mixture Model (GMM). Dynamically model the speech signal, describe the time domain jump of the pronunciation state, and use the mixed Gaussian model to fit the characteristic distribution of each pronunciation state, because this method makes good use of the short-term stationary characteristics of the speech signal, so In the past few decades, it has become the core technology of acoustic modeling in speech recognition. ...

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Problems solved by technology

[0005] Based on the development of deep learning, the time-delayed deep neural network model (TDNN) was applied to acoustic modeling and achieved good results, but the TDNN model did not explicitly model the relative importance of inter-layer features, so In this regard, the performance of acoustic models based on time-delayed neural networks needs to be improved

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