Speech recognition method and system

Abstract

The present invention discloses a speech recognition method and system. The method comprises: obtaining the special type of information text from a pre-determined data source; performing statement segmentation of each obtained information text to obtain a plurality of statements, performing segmentation processing of each statement to obtain corresponding participles, forming a first mapping corpuses through adoption of each statement and the corresponding participles; and according to each obtained first mapping corpus, training the preset type of a first language model, and performing speech recognition based on the trained first language model. The precision of speech recognition is effectively improved, and the cost of speech recognition is effectively reduced.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a speech recognition method and system. Background technique [0002] The language model plays an important role in the speech recognition task. In the existing speech recognition, the language model is generally established by using the marked dialogue text, and the probability of each word is determined through the language model. However, in the prior art, the way of using the marked dialogue text to build a language model, because the current users need to use speech recognition technology in daily life is too few scenarios (for example, the more common scenarios are voice search, voice control and other fields) ), and the type and scope of corpus that can be collected are too concentrated, which makes this method have the following two disadvantages: one is that the purchase price is expensive and the cost is high; the other is that it is difficult to obtain a sufficient a...

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Problems solved by technology

However, in the prior art, the way of using the marked dialogue text to build a language model, because the current users need to use speech recognition technology in daily life is too few scenarios (for example, the more common scenarios are voice search, voice control and other fields) ), and the type and scope of corpus that can be collected are too concentrated, which makes this method have the following two disadvantages: one is that the purchase price is expensive and the cost is high; the other is that it is difficult to obtain a sufficient amount of corpus and obtain marked dialogues The text is difficult, and the timeliness and accuracy of the upgrade and expansion are difficult to guarantee, which in turn affects the training effect and recognition accuracy of the language model, thereby affecting the accuracy of speech recognition

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