Modeling approach and modeling system of acoustic model used in speech recognition

Abstract

The invention relates to a modeling approach and a modeling system of an acoustic model used in speech recognition. The modeling approach includes the steps of: S1, training an initial model, wherein a modeling unit is a tri-phone state which is clustered by a phoneme decision tree and a state transition probability is provided by the model, S2, obtaining state information of a frame level based on the fact that the initial model aligns the tri-phone state of phonetic features of training data compulsively, S3, pre-training a deep neural network to obtain initial weights of each hidden layer, S4, training the initialized network through error back propagation algorithm based on the obtained frame level state information and updating the weights. According to the modeling approach, a context relevant tri-phone state is used as the modeling unit, the model is established based on the deep neural network, weight of each hidden layer of the network is initialized through restricted Boltzmann algorithm, and the weights can be updated subsequently by means of error back propagation algorithm. Therefore, risk that the network is easy to get into local extremum in pre-training is relieved effectively, and modeling accuracy of the acoustic model is improved greatly.

Description

technical field [0001] The invention relates to the field of speech recognition, in particular to a modeling method and modeling system of an acoustic model for speech recognition. Background technique [0002] The current mainstream framework for speech recognition is based on statistical pattern recognition. A typical speech recognition system framework such as figure 1 Shown: including speech acquisition and front-end processing module, feature extraction module, acoustic model module, language model module and decoder module. The basic process of speech recognition is as follows: the speech collection device collects the human speech and performs feature extraction after front-end processing. The extracted feature sequence, such as MFCC or PLP, obtains its observation probability through the acoustic model, and sends it to the decoder in combination with the language model probability to obtain the most effective possible text sequences. The modeling of the acoustic m...

AI Technical Summary

Problems solved by technology

It can be seen that the traditional acoustic model has low modeling accuracy, resulting in poor speech recognition effect

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