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Voice recognition method based on layered circulation neural network language model

A cyclic neural network and language model technology, applied in biological neural network models, speech recognition, neural learning methods, etc., can solve the problems of low recognition accuracy and large storage space.

Inactive Publication Date: 2017-05-31
SHENZHEN WEITESHI TECH
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Problems solved by technology

[0004] Aiming at the problems of low recognition accuracy and large storage space, the purpose of the present invention is to provide a speech recognition method based on a layered recurrent neural network language model. First, the character-level language modeling of RNN is used, and then an external clock and Reset signal extended RNN structure, character-level language modeling with hierarchical RNN, and finally speech recognition

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  • Voice recognition method based on layered circulation neural network language model
  • Voice recognition method based on layered circulation neural network language model
  • Voice recognition method based on layered circulation neural network language model

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Embodiment Construction

[0048] It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present invention will be further described in detail below in conjunction with the drawings and specific embodiments.

[0049] figure 1 It is a system flowchart of a speech recognition method based on a layered recurrent neural network language model of the present invention. Mainly including character-level language modeling using RNN, extending RNN structure with external clock and reset signal, character-level language modeling with hierarchical RNN and performing speech recognition.

[0050] Among them, the described extended RNN structure with external clock and reset signals, most types of RNNs can be generalized as

[0051] the s t =f(x t ,s t-1 ) (1)

[0052] the y t =g(s t ) (2)

[0053] where x t is the input, s t is the state, y t is the output at time step t, ...

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Abstract

The invention provides a voice recognition method based on a layered circulation neural network language model. The method mainly comprises steps of character-level language modeling using RNN, expansion of an RNN structure by use of an external clock and a reset signal, character-level language modeling with graded RNN and voice recognition. According to the invention, the traditional single-clock RNN character-level language model is replaced by layered circulation neural network-based language model, so quite high recognition precision is achieved; quantity of parameters is reduced; vocabulary of the language model is huge; required storage space is quite small; and a layered language model can be expanded to process information of quite long period, such as sentences, topics or other contexts.

Description

technical field [0001] The invention relates to the field of speech recognition, in particular to a speech recognition method based on a layered recurrent neural network language model. Background technique [0002] With the development of modern technology, character-level language models (CLMs) based on recurrent neural networks (RNN) are widely used in speech recognition, text generation, and machine translation. It is useful for modeling words not seen in nature. However, their performance is usually much worse than word-level language models (WLMs). Moreover, statistical language models require a large storage space, usually exceeding 1GB, because not only a large number of vocabularies, but also their combinations need to be considered. [0003] The present invention proposes a speech recognition method based on a hierarchical recurrent neural network language model, and its hierarchical RNN architecture is composed of multiple modules with different clock rates. De...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G10L15/16G10L15/183G06N3/08
CPCG06N3/084G10L15/16G10L15/183
Inventor 夏春秋
Owner SHENZHEN WEITESHI TECH
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