Neural network language model training method and device and voice recognition method

Abstract

The invention discloses a neural network language model training method and device and a voice recognition method and relates to the voice recognition technology. The neural network language model training method and device and the voice recognition method aim to solve the problem that in the prior art, when the computation complexity of a neural network language model is lowered, the recognition accuracy of the neural network language model is lowered. According to the technical scheme, in the process of training parameters of the neural network language model, normalization factors of an output layer are adopted to modify a target cost function, and a modified target cost function is obtained; the parameters of the neural network language model are updated according to the modified target cost function to obtain trained target parameters of the neural network language model, wherein the target parameters enable the normalization factors in the trained neural network language model to be constants. The neural network language model training method and device and the voice recognition method can be applied to the neural network voice recognition process.

Description

technical field [0001] The invention relates to speech recognition technology, in particular to a neural network language model training method, device and speech recognition method. Background technique [0002] Speech recognition refers to the process of using a computer to convert a speech signal into a corresponding text. The result of speech recognition depends on the language model used in speech recognition. The language model is a sequence model. In the case of a given historical sequence Accurately predict the next word, or given sentence candidates with errors, pick the one that best matches human language. An ideal language model can guarantee that the speech recognition system outputs reasonable recognition assumptions. [0003] In the neural network language model, each word is mapped into a high-dimensional vector, and words with similar syntax and semantics have better aggregation in high-dimensional space. The neural network language model has achieved very...

AI Technical Summary

Problems solved by technology

However, due to the introduction of human factors, the methods adopted in the prior art will affect the performance of the neural network language model, which will reduce the quality of speech recognition (recognition rate)

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