Automatic voice recognizing method and system

An automatic speech recognition and speech technology, applied in speech recognition, speech analysis, instruments, etc., can solve problems such as difficulty in obtaining recognition results, data offset, and low recognition accuracy, so as to improve recognition accuracy and reduce data offset The effect of the probability

Active Publication Date: 2014-08-06
TENCENT TECH (SHENZHEN) CO LTD +1
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AI Technical Summary

Problems solved by technology

[0009] However, most of the existing speech recognition technologies are based on universal speech recognition applications, that is, to build models for commonly used speech recognition. In this case, the training corpus of the language model is mainly based on data collection and actual user input. Although from To some extent, it better reflects the user's speaking habits, and it often has a better recognition effect on daily expressions; however, since there are few rare words in the training corpus of the language model, such as medica

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  • Automatic voice recognizing method and system
  • Automatic voice recognizing method and system

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Embodiment Construction

[0050] The present invention will be further described in detail below in conjunction with the drawings and specific embodiments.

[0051] figure 2 It is a processing flow chart of the automatic speech recognition method of the present invention. See figure 2 , The process includes:

[0052] Step 201: Perform corpus classification calculation on the raw corpus to obtain more than one classified corpus of different categories. For example, the classification corpus can be divided into person name category, place name category, computer term category, medical term category and so on. For example, "Banlangen" belongs to the medical term category. A word may also belong to multiple categories.

[0053] Step 202: Perform language model training calculations for each classified corpus to obtain more than one corresponding classified language model.

[0054] Step 203: Perform weighted interpolation processing for each of the classification language models according to the degree of unco...

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Abstract

An automatic speech recognition method includes at a computer having one or more processors and a memory for storing one or more programs to be executed by the processors, obtaining a plurality of speech corpus categories through classifying and calculating raw speech corpus (801); obtaining a plurality of classified language models that respectively correspond to the plurality of speech corpus categories through language model training applied on each speech corpus category (802); obtaining an interpolation language model through implementing a weighted interpolation on each classified language model and merging the interpolated plurality of classified language models (803); constructing a decoding resource in accordance with an acoustic model and the interpolation language model (804); decoding input speech using the decoding resource, and outputting a character string with a highest probability as the recognition result of the input speech (805).

Description

Technical field [0001] This application relates to the technical field of Automatic Speech Recognition (ASR), and in particular to an automatic speech recognition method and system. Background technique [0002] Automatic speech recognition technology is a technology that converts vocabulary content in human speech into computer-readable input characters. Speech recognition has a complicated processing flow, which mainly includes four processes: acoustic model training, language model training, decoding resource construction, and decoding. figure 1 It is a schematic diagram of a main processing flow of an existing automatic speech recognition system. See figure 1 , The main process includes: [0003] In steps 101 and 102, it is necessary to perform acoustic model training based on acoustic raw materials to obtain an acoustic model, and perform language model training based on raw corpus to obtain a language model. [0004] The acoustic model is one of the most important parts of th...

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Application Information

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IPC IPC(8): G10L15/02G10L15/06G10L21/06
CPCG10L15/02G10L15/26G10L15/22G10L15/00G10L15/14G10L15/197
Inventor 饶丰卢鲤陈波岳帅张翔王尔玉谢达东李露陆读羚
Owner TENCENT TECH (SHENZHEN) CO LTD
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