CN114138160A - Learning equipment interacting with user based on multiple modules - Google Patents

Learning equipment interacting with user based on multiple modules Download PDF

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CN114138160A
CN114138160A CN202110992516.9A CN202110992516A CN114138160A CN 114138160 A CN114138160 A CN 114138160A CN 202110992516 A CN202110992516 A CN 202110992516A CN 114138160 A CN114138160 A CN 114138160A
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user
module
information
learning
interacting
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郝海斌
张奎奎
马甲勇
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Suzhou Explore Culture Technology Co ltd
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Suzhou Explore Culture Technology Co ltd
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    • G06F3/0488—Interaction techniques based on graphical user interfaces [GUI] using specific features provided by the input device, e.g. functions controlled by the rotation of a mouse with dual sensing arrangements, or of the nature of the input device, e.g. tap gestures based on pressure sensed by a digitiser using a touch-screen or digitiser, e.g. input of commands through traced gestures
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    • G06F16/9535—Search customisation based on user profiles and personalisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/955—Retrieval from the web using information identifiers, e.g. uniform resource locators [URL]
    • G06F16/9562—Bookmark management
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    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/20—Education
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    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/06—Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice
    • G10L15/063—Training
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue

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Abstract

The present disclosure provides a learning device interacting with a user based on multiple modules, comprising a learning module, wherein the learning module comprises a plurality of sub-learning modules for the user to learn; the terminal comprises an Internet of things module, a communication module and a communication module, wherein the Internet of things module is configured to establish connection with a plurality of terminals; the voice recognition module is configured to receive and recognize user voice information so that the learning equipment interacting with the user based on the multiple modules determines a user instruction according to the user voice information; the bookmark exchange module is configured to provide bookmarks for a user when the user performs bookmark exchange operation; a data statistics module configured to sort, analyze, and present user experience data; a background management module configured to upload local activity information. The learning equipment based on multi-module interaction with the user has fine and smooth and comfortable interactive experience, and is favorable for improving the user experience.

Description

Learning equipment interacting with user based on multiple modules
Technical Field
The present disclosure relates to the field of information display technologies, and in particular, to a learning device interacting with a user based on multiple modules.
Background
The existing learning interaction is mainly displayed in a paper file or single multimedia mode, the form is single, the learning effect is not obvious, and the existing learning interaction has no proper place or fixed place and only aims at specific audiences.
The information disclosed in this background section is only for enhancement of understanding of the general background of the application and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
The embodiment of the disclosure provides a learning device interacting with a user based on multiple modules, which can solve the problems in the prior art.
The embodiment of the present disclosure provides a learning device interacting with a user based on multiple modules, where the learning device interacting with the user based on multiple modules includes:
the learning module comprises a plurality of sub-learning modules for users to learn;
an IOT module configured to enable the multi-module interaction with user-based learning device to establish connections with a plurality of terminals;
a speech recognition module configured to receive and recognize user speech information to cause the multi-module based learning device to interact with a user to determine user instructions from the user speech information;
the bookmark exchange module is configured to provide bookmarks for a user when the user performs bookmark exchange operation;
a data statistics module configured to sort, analyze, and present user experience data;
a background management module configured to upload local activity information.
In an alternative embodiment of the method according to the invention,
the speech recognition module is further configured to:
and recognizing user voice information based on a voice recognition model preset in the voice recognition module, and converting the user voice information into a user instruction so that the learning equipment interacting with the user based on the multiple modules executes the user instruction.
In an alternative embodiment of the method according to the invention,
before the speech recognition model recognizes the speech information of the user, the method further comprises the following steps:
training the voice recognition model based on a preset training data set, wherein the training method comprises the following steps:
acquiring a feature vector corresponding to each training data in a preset training data set based on the preset training data set, wherein the preset training data set comprises user voice information with multiple band labels;
classifying the feature vectors through the voice recognition model to be trained based on the feature vectors corresponding to the training data, and iteratively optimizing a loss function of the voice recognition model to be trained through a preset training algorithm so as to enable a result output by the loss function to accord with a preset threshold value.
In an alternative embodiment of the method according to the invention,
the speech recognition module is further configured to:
matching the acquired user voice information with the user voice information stored in the background database of the learning equipment interacting with the user based on the multiple modules,
and if the matching is successful, acquiring the historical information of the user, and displaying the learning information for the user based on the historical information.
In an alternative embodiment of the method according to the invention,
the multi-module-based learning device interacting with a user further comprises a login module configured to:
the user logs in the system platform of the learning equipment based on the multi-module interaction with the user through the login module based on the social software;
and calling attribute information of a login user through the system platform, and providing preference information corresponding to the attribute information for the user according to the attribute information.
In an alternative embodiment of the method according to the invention,
the data statistics module is further configured to:
summarizing at least one of operation information, user voice information and user login information of a user on learning equipment interacting with the user based on multiple modules;
based on a big data analysis technology, performing user portrait on a user according to operation information, user voice information and user login information of the user on learning equipment interacting with the user on the basis of multiple modules, and determining each user portrait;
based on each user profile, at least one of learning information, historical operating information, and preference information that is consistent with the user profile is provided to the user.
In an alternative embodiment of the method according to the invention,
the learning device interacting with the user based on multiple modules further comprises:
the touch control display module is arranged to display an interactive interface for guiding a user to perform learning operation and respond to the touch control operation of the user;
the voice acquisition module is arranged to be connected with the touch display equipment so as to perform voice control on the touch display equipment;
and the infrared identification module is arranged to be connected with the touch display equipment so as to start the touch display equipment when a user is identified.
The learning equipment based on the multi-module interaction with the user comprises a learning module, wherein the learning module comprises a plurality of sub-learning modules for the user to learn; an IOT module configured to enable the multi-module interaction with user-based learning device to establish connections with a plurality of terminals; a speech recognition module configured to receive and recognize user speech information to cause the multi-module based learning device to interact with a user to determine user instructions from the user speech information; the bookmark exchange module is configured to provide bookmarks for a user when the user performs bookmark exchange operation; a data statistics module configured to sort, analyze, and present user experience data; a background management module configured to upload local activity information.
The internet of things module can be connected with a plurality of terminals, so that the information of the internet of things module can be synchronized to other terminals, or other terminals can be accessed to the learning equipment which is based on interaction between the internet of things module and a user, and information sharing is realized;
the voice recognition module of the present disclosure is capable of receiving and recognizing user voice information so that a user instruction can be determined according to the user voice information; in addition, the voice module can also match the voice information of the user with the pre-stored voice information and display the learning information matched with the voice information for the user; before voice recognition is carried out, a voice recognition model in the voice recognition module can be trained, and the accuracy of the voice recognition is improved;
according to the bookmark exchange component, the bookmark is outwards exported when the user exchanges the bookmark, and party history is further output in the form of the bookmark, so that the user can learn related knowledge anytime and anywhere when using the bookmark;
the data statistics module can portray the user through a big data analysis technology, provides learning information, historical operation information and preference information which accord with the portrayal of the user for the user, is favorable for improving user experience, increases the learning interest of the user, and is favorable for improving the frequency and probability of active learning of the user to a certain extent.
The learning equipment based on interaction between the multiple modules and the user has fine and smooth and comfortable interactive experience, is favorable for improving the user experience, and can solve the problems in the prior art.
Drawings
FIG. 1 is a schematic structural diagram of a learning device interacting with a user based on multiple modules according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of a learning device interacting with a user based on multiple modules according to an embodiment of the present disclosure;
fig. 3 is a schematic diagram of data statistics according to an embodiment of the disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be described clearly and completely with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims of the present disclosure and in the drawings described above, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the disclosure described herein are capable of operation in sequences other than those illustrated or otherwise described herein.
It should be understood that, in various embodiments of the present disclosure, the sequence numbers of the processes do not mean the execution sequence, and the execution sequence of the processes should be determined by the functions and the inherent logic of the processes, and should not constitute any limitation on the implementation process of the embodiments of the present disclosure.
It should be understood that in the present disclosure, "including" and "having" and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in the present disclosure, "plurality" means two or more. "and/or" is merely an association describing an associated object, meaning that three relationships may exist, for example, and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "comprises A, B and C" and "comprises A, B, C" means that all three of A, B, C comprise, "comprises A, B or C" means that one of A, B, C comprises, "comprises A, B and/or C" means that any 1 or any 2 or 3 of A, B, C comprises.
It should be understood that in this disclosure, "B corresponding to a", "a corresponds to B", or "B corresponds to a" means that B is associated with a, from which B can be determined. Determining B from a does not mean determining B from a alone, but may also be determined from a and/or other information. And the matching of A and B means that the similarity of A and B is greater than or equal to a preset threshold value.
As used herein, "if" may be interpreted as "at … …" or "when … …" or "in response to a determination" or "in response to a detection", depending on the context.
The technical solution of the present disclosure is explained in detail below with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.
The embodiment of the disclosure provides a learning device interacting with a user based on multiple modules.
Fig. 1 is a schematic structural diagram of a learning device interacting with a user based on multiple modules according to an embodiment of the present disclosure, where as shown in fig. 1, the learning device interacting with the user based on multiple modules includes:
a learning module 11, wherein the learning module 11 comprises a plurality of sub-learning modules for a user to learn;
an internet of things module 12, the internet of things module 12 being configured to enable the multi-module interaction with user-based learning device to establish a connection with a plurality of terminals;
a speech recognition module 13, wherein the speech recognition module 13 is configured to receive and recognize user speech information, so that the learning device interacting with the user based on the multiple modules determines a user instruction according to the user speech information;
the bookmark exchange module 14 is configured to provide a bookmark to the user when the user performs bookmark exchange operation;
a data statistics module 15, said data statistics module 15 configured to sort, analyze and present user experience data;
a back-office management module 16, said back-office management module 16 configured to upload local activity information.
Fig. 2 schematically illustrates a physical schematic diagram of a learning device interacting with a user based on multiple modules according to an embodiment of the present disclosure. The study equipment based on interaction between the multi-module and the user is integrally made of solid wood, fine in working, 27-inch capacitive interaction screen, fine and comfortable in interaction experience, novel in product design, simple and unreduced, and classic and elegant.
Illustratively, the device size of the learning device of the embodiment of the present disclosure based on multiple modules interacting with the user: length 67cm wide by 39cm high 112 cm. It should be noted that, the above parameters of the learning apparatus interacting with the user based on multiple modules in the embodiment of the present disclosure are merely exemplary, and the specific size of the learning apparatus interacting with the user based on multiple modules in the embodiment of the present disclosure is not limited.
As shown in fig. 2, the front side of the operation table base in fig. 2 can be provided with an exchangeable pattern logo, wherein the exchangeable pattern logo is set based on user requirements.
In practical applications, the logo on the front side of the operation table can be replaced by any pattern, including but not limited to logo, party logo, etc. according to the characteristics of the shop or the local, which is not limited by the embodiment of the present disclosure.
In an alternative embodiment of the method according to the invention,
the speech recognition module is further configured to:
and recognizing user voice information based on a voice recognition model preset in the voice recognition module, and converting the user voice information into a user instruction so that the learning equipment interacting with the user based on the multiple modules executes the user instruction.
Exemplarily, the embodiment of the disclosure supports voice control of learning equipment interacting with a user based on multiple modules, omits a step of manual operation by the user through the voice control, and is friendly to user experience.
In an alternative embodiment of the method according to the invention,
before the speech recognition model recognizes the speech information of the user, the method further comprises the following steps:
training the voice recognition model based on a preset training data set, wherein the training method comprises the following steps:
acquiring a feature vector corresponding to each training data in a preset training data set based on the preset training data set, wherein the preset training data set comprises user voice information with multiple band labels;
classifying the feature vectors through the voice recognition model to be trained based on the feature vectors corresponding to the training data, and iteratively optimizing a loss function of the voice recognition model to be trained through a preset training algorithm so as to enable a result output by the loss function to accord with a preset threshold value.
By training the voice recognition model, the accuracy of voice recognition of the voice recognition model can be improved, the voice of the user can be recognized accurately and converted into the corresponding user instruction, and the user experience is improved.
In an alternative embodiment of the method according to the invention,
the speech recognition module is further configured to:
matching the acquired user voice information with the user voice information stored in the background database of the learning equipment interacting with the user based on the multiple modules,
and if the matching is successful, acquiring the historical information of the user, and displaying the learning information for the user based on the historical information.
Exemplarily, a plurality of user voice information can be stored in a learning device background database interacting with a user based on multiple modules, the currently acquired user voice information is matched with the pre-stored user voice information, if the matching is successful, the history information of the user can be directly acquired, and corresponding learning information is displayed for the user based on the history information of the user;
specifically, based on the historical information of the user, the use condition or the interest degree of the user on a certain module or a certain aspect of content can be known, so that related data can be pushed or recommended for the user, the step of searching the data again by the user is omitted, and the learning habit and the persistence of the user are continued.
In an alternative embodiment of the method according to the invention,
the multi-module-based learning device interacting with a user further comprises a login module configured to:
the user logs in the system platform of the learning equipment based on the multi-module interaction with the user through the login module based on the social software;
and calling attribute information of a login user through the system platform, and providing preference information corresponding to the attribute information for the user according to the attribute information.
Illustratively, the embodiment of the disclosure can provide preference information corresponding to attribute information for a user by calling the attribute information of a login user;
in addition, after the user logs in, the user can share the information such as the final score of the answer and the like through social software after the answer is finished, so that more people can know the learning equipment based on interaction between the multi-module and the user;
the learning equipment based on interaction between the multiple modules and the user also supports a non-login mode for learning and carries out answer experience through a tourist mode.
In an alternative embodiment of the method according to the invention,
the data statistics module is further configured to:
summarizing at least one of operation information, user voice information and user login information of a user on learning equipment interacting with the user based on multiple modules;
based on a big data analysis technology, performing user portrait on a user according to operation information, user voice information and user login information of the user on learning equipment interacting with the user on the basis of multiple modules, and determining each user portrait;
based on each user profile, at least one of learning information, historical operating information, and preference information that is consistent with the user profile is provided to the user.
By performing user representation on the user, at least one of learning information, historical operation information and preference information which are in accordance with the user representation can be provided for the user, and targeted information can be provided for the user,
in an alternative embodiment of the method according to the invention,
the learning device interacting with the user based on multiple modules further comprises:
the touch control display module is arranged to display an interactive interface for guiding a user to perform learning operation and respond to the touch control operation of the user;
the voice acquisition module is arranged to be connected with the touch display equipment so as to perform voice control on the touch display equipment;
and the infrared identification module is arranged to be connected with the touch display equipment so as to start the touch display equipment when a user is identified.
The touch display module can guide a user to learn and operate the interactive interface, the user experience is increased in the process, the learning equipment interacting with the user based on the multiple modules can respond to the touch operation of the user in the process of learning and operating the interactive interface, and the user can learn more easily and is more interested in related knowledge based on the interactive operation;
the voice acquisition module is used for performing voice control on the touch display equipment, the step of manual operation of a user is omitted through the voice control, the user experience is friendly, and in addition, the user can accurately know the content to be known currently through voice, so that the impression of learning knowledge can be deepened;
the infrared identification module can identify a user and then starts the touch display device, so that the user can use the learning device based on interaction between the multi-module and the user within a certain range, the improvement of user experience is facilitated, and the frequency and probability of active learning of the user are increased to a certain extent.
The learning equipment based on the multi-module interaction with the user comprises a learning module, wherein the learning module comprises a plurality of sub-learning modules for the user to learn; an IOT module configured to enable the multi-module interaction with user-based learning device to establish connections with a plurality of terminals; a speech recognition module configured to receive and recognize user speech information to cause the multi-module based learning device to interact with a user to determine user instructions from the user speech information; the bookmark exchange module is configured to provide bookmarks for a user when the user performs bookmark exchange operation; a data statistics module configured to sort, analyze, and present user experience data; a background management module configured to upload local activity information.
The learning equipment based on interaction between the multiple modules and the user has fine and smooth and comfortable interactive experience, is favorable for improving the user experience, and can solve the problems in the prior art.
Illustratively, the functions of the respective modules of the learning system of the embodiment of the present disclosure are as follows:
platform login: WeChat login is performed, interactivity is increased, the user can get on and off the line through a communication line, and secondary chain propagation is performed;
the Internet of things module: the Internet of things technology does not depend on a venue network environment, and the stability of experience is ensured;
and (3) voice recognition: the experience is more scientific and technical, the query is more convenient, and the interaction is more diversified;
bookmark redemption: integrating and changing bookmarks, increasing participation, increasing memory, and performing secondary propagation;
and (3) data statistics: the user experience data of every day, every week and every month is processed and analyzed by using a chart to realize data visual display;
background management: local activity information can be uploaded, and background management and maintenance are facilitated.
The device can initially bind to an administrator through the social platform and has administrator permission; the device interface can be set to display the name of a user, the address of the user is set, and devices with the same address can be automatically grouped; points required by exchange can be set, and bookmarks are added; the explanation mode of an administrator can be set, the question bank resources are fixed, and explanation is convenient.
The learning system disclosed by the embodiment of the disclosure has the advantages that the module function is open, the characteristic resources can be automatically uploaded by using the background, and the learning platform conforming to the characteristics of the learning system can be provided without customization.
Fig. 3 is a schematic diagram illustrating data statistics according to an embodiment of the disclosure.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present disclosure, and not for limiting the same; while the present disclosure has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present disclosure.

Claims (7)

1. A learning device interacting with a user based on multiple modules, the learning device interacting with the user based on multiple modules comprises:
the learning module comprises a plurality of sub-learning modules for users to learn;
an IOT module configured to enable the multi-module interaction with user-based learning device to establish connections with a plurality of terminals;
a speech recognition module configured to receive and recognize user speech information to cause the multi-module based learning device to interact with a user to determine user instructions from the user speech information;
the bookmark exchange module is configured to provide bookmarks for a user when the user performs bookmark exchange operation;
a data statistics module configured to sort, analyze, and present user experience data;
a background management module configured to upload local activity information.
2. The multi-module-based learning device for interacting with a user of claim 1, wherein the speech recognition module is further configured to:
and recognizing user voice information based on a voice recognition model preset in the voice recognition module, and converting the user voice information into a user instruction so that the learning equipment interacting with the user based on the multiple modules executes the user instruction.
3. The multi-module based learning device for interacting with a user according to claim 1, wherein before the speech recognition model recognizes the user's speech information, the method further comprises:
training the voice recognition model based on a preset training data set, wherein the training method comprises the following steps:
acquiring a feature vector corresponding to each training data in a preset training data set based on the preset training data set, wherein the preset training data set comprises user voice information with multiple band labels;
classifying the feature vectors through the voice recognition model to be trained based on the feature vectors corresponding to the training data, and iteratively optimizing a loss function of the voice recognition model to be trained through a preset training algorithm so as to enable a result output by the loss function to accord with a preset threshold value.
4. The multi-module-based learning device for interacting with a user of claim 1, wherein the speech recognition module is further configured to:
matching the acquired user voice information with the user voice information stored in the background database of the learning equipment interacting with the user based on the multiple modules,
and if the matching is successful, acquiring the historical information of the user, and displaying the learning information for the user based on the historical information.
5. The multi-module interaction based learning device of claim 1, further comprising a login module configured to:
the user logs in the system platform of the learning equipment based on the multi-module interaction with the user through the login module based on the social software;
and calling attribute information of a login user through the system platform, and providing preference information corresponding to the attribute information for the user according to the attribute information.
6. The multi-module-based learning device for interacting with a user of claim 1, wherein the data statistics module is further configured to:
summarizing at least one of operation information, user voice information and user login information of a user on learning equipment interacting with the user based on multiple modules;
based on a big data analysis technology, performing user portrait on a user according to operation information, user voice information and user login information of the user on learning equipment interacting with the user on the basis of multiple modules, and determining each user portrait;
based on each user profile, at least one of learning information, historical operating information, and preference information that is consistent with the user profile is provided to the user.
7. The multi-module interaction based learning device of claim 1, further comprising:
the touch control display module is arranged to display an interactive interface for guiding a user to perform learning operation and respond to the touch control operation of the user;
the voice acquisition module is arranged to be connected with the touch display equipment so as to perform voice control on the touch display equipment;
and the infrared identification module is arranged to be connected with the touch display equipment so as to start the touch display equipment when a user is identified.
CN202110992516.9A 2021-08-27 2021-08-27 Learning equipment interacting with user based on multiple modules Pending CN114138160A (en)

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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2017041372A1 (en) * 2015-09-07 2017-03-16 百度在线网络技术（北京）有限公司 Man-machine interaction method and system based on artificial intelligence
CN109410911A (en) * 2018-09-13 2019-03-01 何艳玲 Artificial intelligence learning method based on speech recognition
CN111627440A (en) * 2020-05-25 2020-09-04 红船科技(广州)有限公司 Learning system for realizing interaction based on three-dimensional virtual character and voice recognition
CN111652620A (en) * 2020-04-15 2020-09-11 珠海小礼鱼科技有限公司 Intelligent terminal interaction system
CN111914072A (en) * 2020-07-14 2020-11-10 青岛聚好联科技有限公司 Information interaction method, equipment and device
CN113223502A (en) * 2021-04-28 2021-08-06 平安科技（深圳）有限公司 Speech recognition system optimization method, device, equipment and readable storage medium

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2017041372A1 (en) * 2015-09-07 2017-03-16 百度在线网络技术（北京）有限公司 Man-machine interaction method and system based on artificial intelligence
CN109410911A (en) * 2018-09-13 2019-03-01 何艳玲 Artificial intelligence learning method based on speech recognition
CN111652620A (en) * 2020-04-15 2020-09-11 珠海小礼鱼科技有限公司 Intelligent terminal interaction system
CN111627440A (en) * 2020-05-25 2020-09-04 红船科技(广州)有限公司 Learning system for realizing interaction based on three-dimensional virtual character and voice recognition
CN111914072A (en) * 2020-07-14 2020-11-10 青岛聚好联科技有限公司 Information interaction method, equipment and device
CN113223502A (en) * 2021-04-28 2021-08-06 平安科技（深圳）有限公司 Speech recognition system optimization method, device, equipment and readable storage medium

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