Speech recognition method and system, electronic equipment and storage medium

A speech recognition and speech technology, applied in speech recognition, speech analysis, instruments, etc., can solve the problems of low recognition accuracy and inability to recognize speech, and achieve the effect of improving recognition accuracy, low construction cost, and wide application range.

Pending Publication Date: 2020-09-01
携程旅游信息技术(上海)有限公司
View PDF15 Cites 9 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In view of the above-mentioned deficiencies in the prior art, the purpose of the present invention is to provide an improved speech recognition method, system, electronic equipment and storage medium to solve the problem of inability to perform targeted speech recognition for specific business scenarios of users, and the recognition accuracy is not high The problem

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Speech recognition method and system, electronic equipment and storage medium
  • Speech recognition method and system, electronic equipment and storage medium
  • Speech recognition method and system, electronic equipment and storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0063] This embodiment provides a speech recognition method, such as figure 1 As shown, it specifically includes the following steps:

[0064] S1. Obtain training sample sets of different scenarios, and each training sample set includes several training voices and text labels corresponding to each training voice.

[0065] In this embodiment, different scenarios may be business scenarios such as air ticket reservation, hotel reservation, travel reservation, and train ticket reservation. Wherein, the training voice of the scene of ticket reservation may come from the historical voice record of ticket reservation, and the training voice is pre-marked with a corresponding text label. In a similar manner, training sample sets for scenarios such as hotel reservations, travel reservations, and train ticket reservations can also be obtained.

[0066] S2. Perform preprocessing on each training sample set, specifically including: extracting spectral features of each training voice in ea...

Embodiment 2

[0083] This embodiment provides a speech recognition system 10, such as figure 2 As shown, the system 10 includes:

[0084] Sample acquisition module 11, for obtaining the training sample set of different scenes, described training sample set comprises some training voices and text labels corresponding to the training voices;

[0085] A preprocessing module 12, configured to preprocess each of the training sample sets

[0086] The model training module 13 is used to train the preset machine learning models respectively according to the training sample sets of different scenes, so as to obtain semantic models corresponding to different scenes;

[0087] Voice acquiring module 14, for acquiring the voice to be recognized, the voice to be recognized carries scene label;

[0088] A semantic model determination module 15, configured to acquire a semantic model corresponding to the scene label from the semantic models corresponding to the different scenes;

[0089] A model proces...

Embodiment 3

[0112] This embodiment provides an electronic device, which can be expressed in the form of a computing device (for example, it can be a server device), including a memory, a processor, and a computer program stored on the memory and operable on the processor, wherein the processor The speech recognition method provided in Embodiment 1 can be realized when the computer program is executed.

[0113] image 3 A schematic diagram of the hardware structure of this embodiment is shown, as image 3As shown, the electronic device 9 specifically includes:

[0114] At least one processor 91, at least one memory 92, and a bus 93 for connecting different system components, including the processor 91 and the memory 92, wherein:

[0115] The bus 93 includes a data bus, an address bus, and a control bus.

[0116] The memory 92 includes a volatile memory, such as a random access memory (RAM) 921 and / or a cache memory 922 , and may further include a read only memory (ROM) 923 .

[0117] M...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention provides a voice recognition method and system, electronic equipment and a storage medium, and the method comprises the steps: obtaining training sample sets of different scenes, and enabling the training sample sets to comprise a plurality of training voices and text labels corresponding to the training voices; training a preset machine learning model according to the training sample sets of the different scenes to obtain semantic models corresponding to the different scenes; to-be-recognized voice is obtained, wherein the to-be-recognized voice carries a scene label; obtainingsemantic models corresponding to the scene labels from the semantic models corresponding to the different scenes; processing the to-be-recognized voice by utilizing the target semantic model to obtainan initial recognition result of the to-be-recognized voice; and performing calibration processing on the initial recognition result by using a preset language model to obtain a target recognition result of the to-be-recognized voice. According to the invention, the problems that targeted voice recognition cannot be carried out for a specific service scene of a user and the recognition accuracy is not high can be solved.

Description

technical field [0001] The invention relates to the technical field of voice recognition, in particular to a voice recognition method, system, electronic equipment and storage medium. Background technique [0002] At present, with the business development needs of various companies, there are more and more application scenarios for speech recognition technology, especially in the field of call centers, such as intelligent voice customer service, customer service recording quality inspection, and outbound call failure analysis. application. In different application scenarios, words with the same pronunciation may have different meanings. [0003] Traditional speech recognition technology generally relies on various complex model designs, including acoustic models and hidden Markov models (HMM). These models need to be built by specialized companies for enterprise users. Not only are the construction costs high, and special voice formats are limited, but most importantly, th...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G10L15/06G10L15/16G10L15/183G10L15/26
CPCG10L15/063G10L15/183G10L15/26G10L15/16Y02T10/40
Inventor 华吉春赵桦
Owner 携程旅游信息技术(上海)有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap