CN107239897A - A kind of personality occupation type method of testing and system - Google Patents
A kind of personality occupation type method of testing and system Download PDFInfo
- Publication number
- CN107239897A CN107239897A CN201710398626.6A CN201710398626A CN107239897A CN 107239897 A CN107239897 A CN 107239897A CN 201710398626 A CN201710398626 A CN 201710398626A CN 107239897 A CN107239897 A CN 107239897A
- Authority
- CN
- China
- Prior art keywords
- user
- answer
- audio
- topic
- personality
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
-
- 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
- G06F16/903—Querying
- G06F16/90335—Query processing
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Databases & Information Systems (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Marketing (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Computational Linguistics (AREA)
- Game Theory and Decision Science (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The present invention provides a kind of personality occupation type method of testing and system, and methods described includes：Obtain the answer type of user's selection；Multiple topics with the answer type matching are found from the exam pool of database, so that user is answered；The answer that user answers to each topic is obtained by speech recognition technology, and scored according to answer；According to the score of each topic, the total score after user is answered all types topic is obtained, and corresponding personality occupational group is matched according to total score.With reference to the own situation of user, the topic for providing the user respective type is answered, during user's answer, user is obtained using speech recognition technology to answer to each problem purpose answer, and then the fraction of user's answer is drawn according to answer of answering, the corresponding personality type of user is gone out according to fractional matching so that user refers to the more clear cognition of scoring and personality occupation type and arrives itself personality type, is easy to the occupation of reasonable selection oneself.
Description
Technical field
The present invention relates to evaluation technology field, more particularly, to a kind of personality occupation type method of testing and system.
Background technology
When job hunter faces the critical period chosen a job, or when facing two or more occupation/unit/position/post selection,
Most people is the body to oneself being adapted to which type of work, the personality of oneself can be pleased in what course of work in fact
Test assurance unclear.
Therefore, many experts are to take certain assessment method to test the personality of job hunter, but main at present
It is, by artificial man-to-man detection, subjective evaluation to be carried out to the personality of job hunter by expert.
The method judged by expert the personality of job hunter, there is subjectivity, random and limitation etc. in it
Whether deficiency, the judge made correctly is also difficult to hold, and is detected in addition by professional person is one-to-one, its exist cost it is high,
The low problem of efficiency.
The content of the invention
The present invention provides a kind of personality occupation type for overcoming above mentioned problem or solving the above problems at least in part and surveyed
Method for testing and system.
According to an aspect of the invention, there is provided a kind of personality occupation type method of testing, including：
S1, obtains the answer type of user's selection, and the answer type includes a variety of；
S2, finds multiple topics with the answer type matching, so that user is answered from the exam pool of database；
S3, obtains the answer that user answers to each topic, and scored according to answer by speech recognition technology；
S4, according to the score of each topic, obtains the total score after user is answered all types topic, and according to total
Score matches corresponding personality occupational group.
Beneficial effects of the present invention are：With reference to the own situation of user, the topic for providing the user respective type is made
Answer, during user's answer, user is obtained using speech recognition technology and each problem purpose is answered answer, and then according to
Answer of answering draws the fraction of user's answer, goes out the corresponding personality type of user according to fractional matching so that user refers to comment
Divide and personality occupation type is more clear cognitive to itself personality type, be easy to the occupation of reasonable selection oneself.
On the basis of above-mentioned technical proposal, the present invention can also make following improvement.
Further, the step S1 is specifically included：
For each answer type, corresponding glide direction is set；
The glide direction of user is detected, and according to the glide direction, obtains corresponding answer type.
Further, the step S3 is specifically included：
S31, audio-frequency information when collection user answers to each topic；
S32, Similarity Measure is carried out by the audio-frequency information of user and multiple standard audio informations, wherein, the standard pronunciation
Frequency information is corresponding with the answer of each topic；
S33, using topic answer corresponding with the similarity highest standard audio information of the audio-frequency information of user as with
The answer at family.
Further, also include before the step S32：
Processing is filtered to the audio-frequency information of the user of collection using Wiener Filter Method.
Further, the step S32 is specifically included：
Audio-frequency information after filtering process is segmented, multistage sub-audio information is formed；
Each segment standard audio-frequency information is segmented, the standard audio information of multiple segments is formed；
The similarity between each cross-talk audio-frequency information and corresponding every a bit of standard audio information is calculated, will be by institute
Some similarities are added, and obtain the similarity between the audio-frequency information of user and each segment standard audio-frequency information.
Further, the step S32 also includes：
Each cross-talk audio-frequency information is inputted in HMM acoustic models, multigroup characteristic vector is exported, wherein, a cross-talk audio
Information one group of characteristic vector of correspondence；And,
Multigroup characteristic vector will be exported per a bit of standard audio information in input HMM acoustic models.
Further, it is described to calculate between each cross-talk audio-frequency information and corresponding every a bit of standard audio information
Similarity is specifically included：
By the corresponding characteristic vector of each cross-talk audio-frequency information and every a bit of standard pronunciation of each segment standard audio-frequency information
The corresponding characteristic vector of frequency information carries out Similarity Measure.
According to another aspect of the present invention, a kind of personality occupation type test system is additionally provided, including：
First acquisition module, the answer type for obtaining user's selection, the answer type includes a variety of；
First matching module, for finding multiple topics with the answer type matching from the exam pool of database, with
Answered for user；
Second acquisition module, for obtaining the answer that user answers to each topic, and root by speech recognition technology
Scored according to answer；
Second matching module, for the score according to each topic, is obtained after user answered all types topic
Total score, and corresponding personality occupational group is matched according to total score.
Further, first acquisition module includes：
Setting unit, for setting corresponding glide direction for each answer type；
Detection unit, the glide direction for detecting user, and according to the glide direction, obtain corresponding answer class
Type.
Further, second acquisition module includes：
Collecting unit, for gathering audio-frequency information when user answers to each topic；
Computing unit, for the audio-frequency information of user and multiple standard audio informations to be carried out into Similarity Measure, will with
The corresponding topic answer of similarity highest standard audio information of the audio-frequency information at family as user answer, wherein, it is described
Standard audio information is corresponding with the answer of each topic.
Brief description of the drawings
Fig. 1 is the personality occupation type method of testing flow chart of one embodiment of the invention；
Fig. 2 connects block diagram for the personality occupation type test system of another embodiment of the present invention；
Fig. 3 connects for the inside of the first acquisition module in the personality occupation type test system of another embodiment of the present invention
Block diagram；
Fig. 4 connects for the inside of the second acquisition module in the personality occupation type test system of another embodiment of the present invention
Block diagram.
Embodiment
With reference to the accompanying drawings and examples, the embodiment to the present invention is described in further detail.Implement below
Example is used to illustrate the present invention, but is not limited to the scope of the present invention.
Referring to Fig. 1, Fig. 1 provides the personality occupation type method of testing of one embodiment of the invention, including：S1, is obtained
The answer type of user's selection, the answer type includes a variety of；S2, finds and the answer type from the exam pool of database
Multiple topics of matching, so that user is answered；S3, obtains what user answered to each topic by speech recognition technology
Answer, and scored according to answer；S4, according to the score of each topic, is obtained after user answered all types topic
Total score, and corresponding personality occupational group is matched according to total score.
After user's Successful login test platform, user can select answer type in the homepage of test platform, wherein, answer
Topic type has a variety of.In the present embodiment, answer type is broadly divided into six kinds, is respectively actual type, exploring instruction, artistic type, society
Meeting type, enterprise and affairs type, different types of topic can reflect different personality occupation types.Test in the present embodiment
Platform can be the mobile terminals such as touch type mobile phone or tablet personal computer, or go out on the terminal device of model, do not do and have
The limitation of body.
The answer type that test platform is selected according to user, finds many with answer type matching from the exam pool of database
Individual topic.Wherein, be stored with the corresponding a set of topic of polytype in database, and the topic of each type has many.
The topic types selected according to user, have found after corresponding a set of topic from database, in a certain order by these
Topic is shown on answer interface, so that user is answered.Such as, in first topic of answer interface display, when user
Answer after first topic, then shown second topic, answered for user, by that analogy, until all topics of this type
Mesh, which is answered, to be finished.
During user carries out answer, obtain user using speech recognition technology and answer to each problem purpose to answer
Case, and scored according to answer of answering.After user answers to all topics of one of which type to be finished, test platform meeting
Return to the homepage, user selects answer type again, repeat above-mentioned process, until user determines to stop answer or pre-sets
6 types topic all answered.After the completion of user answers all types of all topics, can count must
Point, the personality occupation type and directiveness corresponding with this kind of personality occupation type for then matching user according to total score are built
View, support is provided for the occupation choice of subsequent user.
The own situation of the present embodiment combination user, the topic for providing the user respective type is answered, answered in user
During topic, user is obtained using speech recognition technology and each problem purpose is answered answer, and then is obtained according to answer of answering
Go out the fraction of user's answer, the corresponding personality type of user is gone out according to fractional matching so that user refers to scoring and personality duty
Industry type is more clear cognitive to itself personality type, is easy to the occupation of reasonable selection oneself.
In one embodiment of the invention, the step S1 is specifically included：Set corresponding for each answer type
Glide direction；The glide direction of user is detected, and according to the glide direction, obtains corresponding answer type.
During application and development, developer can set an identification image in the homepage of test platform, and be
Identification image sets different glide directions.Such as, as above, 6 kinds of different answer types are provided with the present embodiment, therefore,
It is that identification image sets 6 kinds of different glide directions during exploitation.During setting, slip can be set not
The different directions of slip are realized with angle, such as, the direction of slip can be divided into above and below left and right, upper left, upper right etc..Adopt
The selection of answer type is carried out with glide direction, the interest during whole answer is added.
In another embodiment of the present invention, the step S3 is specifically included：S31, user is to each topic for collection
Audio-frequency information when answering；S32, Similarity Measure is carried out by the audio-frequency information of user and multiple standard audio informations, wherein, institute
The answer that standard audio information is stated with each topic is corresponding；S33, by the similarity highest mark with the audio-frequency information of user
The corresponding topic answer of quasi- audio-frequency information as user answer.
During user answers to each problem purpose, the present embodiment is to obtain user couple using speech recognition technology
Each problem purpose answer answer.Specifically process is:Audio-frequency information when collection user answers to each topic first, than
Such as, it can assist to gather by microphone.Then the audio-frequency information of user and the audio-frequency information of multiple standards are subjected to similarity
Calculate.Wherein, each segment standard audio-frequency information is corresponding with each answer to topic.Such as, standard audio information can be
Satisfied or unsatisfied audio-frequency information.By the phase of the audio-frequency information of the user of collection standard audio information corresponding from different answers
Like degree calculate after, using topic answer corresponding with the similarity highest standard audio information of the audio-frequency information of user as
The answer of user.
In one embodiment of the invention, also include before the step S32：Using use of the Wiener Filter Method to collection
The audio-frequency information at family is filtered processing.
Before the similarity between the audio-frequency information and standard audio information of user is calculated, to the sound of the user to collection
Frequency information is filtered processing, eliminates the influence that partial noise and different speakers bring, makes the audio-frequency information after filtering process
The substantive characteristics of voice can more be reflected, the present embodiment is filtered place using Wiener Filter Method to the audio-frequency information of the user of collection
Reason.
In one embodiment of the invention, the step S32 is specifically included：Audio-frequency information after filtering process is carried out
Segmentation, forms multistage sub-audio information；Each segment standard audio-frequency information is segmented, the standard audio letter of multiple segments is formed
Breath；Each cross-talk audio-frequency information and the corresponding similarity per between a bit of standard audio information are calculated, by will be all
Similarity is added, and obtains the similarity between the audio-frequency information of user and each segment standard audio-frequency information.
, will be right during the similarity between the audio-frequency information and each segment standard audio-frequency information of user is specifically calculated
Audio user information after filtering process is segmented, and forms multistage sub-audio information；And by each segment standard audio-frequency information
It is segmented, forms the standard audio information of multiple segments, wherein, the hop count and audio user of the standard audio information after division
The subsegment number of information is equal.
After audio-frequency information and each segment standard audio-frequency information respectively to user is segmented, each cross-talk audio is calculated
Information and the corresponding similarity per between a bit of standard audio information, by the sub-audio information of all hop counts and all small
Similarity between the standard audio information of section is added, and is obtained between the audio-frequency information of user and each segment standard audio-frequency information
Similarity.
In another embodiment of the present invention, the step S32 also includes：Each cross-talk audio-frequency information is inputted into HMM
In acoustic model, multigroup characteristic vector is exported, wherein, a cross-talk audio-frequency information one group of characteristic vector of correspondence；And, will be each small
In segment standard audio-frequency information input HMM acoustic models, multigroup characteristic vector is exported.
During similarity is calculated, each cross-talk audio-frequency information is inputted in HMM acoustic models, and is fallen using Mel
Spectral coefficient, wherein, HMM acoustic models are Hidden Markov acoustic model, analyze each cross-talk audio-frequency information, obtain and each section
The corresponding one group of characteristic vector of sub-audio information, for multistage sub-audio information, by inputting HMM acoustic models, obtains multigroup
Characteristic vector.Likewise, multigroup characteristic vector will be exported per a bit of standard audio information in input HMM acoustic models.
In one embodiment of the invention, it is described to calculate each cross-talk audio-frequency information with corresponding per a bit of standard
Similarity between audio-frequency information is specifically included：By the corresponding characteristic vector of each cross-talk audio-frequency information and each segment standard audio
The corresponding characteristic vector of every a bit of standard audio information of information carries out Similarity Measure.
Above-described embodiment has obtained multigroup characteristic vector of each cross-talk audio-frequency information of audio user information and each
After multigroup characteristic vector of segment standard audio-frequency information, by the corresponding characteristic vector of each cross-talk audio-frequency information and each segment standard sound
The corresponding characteristic vector of every a bit of standard audio information of frequency information carries out Similarity Measure.Then by the spy of all correspondence groups
The similarity for levying vector is added, and obtains similarity between the audio-frequency information of user and each segment standard audio-frequency information.Finally will be with
The answer of the corresponding topic of audio user information similarity highest standard audio information is answered this topic as user
Case, and obtain the corresponding score of the answer.
Answer the score of each topic, for each type, user is answered in this type when obtaining user
The score of each topic is added and obtains the total score after user answers to such all topics, then by user to every
Total score after one type topic is answered is added, and obtains the total score after user answers to all types of all topics.So
Afterwards calculate user the topic of each type is answered after middle score take family all types of topics are answered after must
Point weighted value=user the topic of each type is answered after total score/user all types of topics are answered after
Total score.Total score and corresponding weighted value after being answered finally according to user to each type topic, obtain corresponding
Personality occupation type, and guidance instruction corresponding with the personality occupation type is got, with for reference.
Below by taking six kinds of answer types of A, B, C, D, E and F as an example, user the topic of A types is answered after total score
For the addition that A is each problem purpose score, the total score of B, C, D, E and F type is obtained according to same algorithm.User couple
What all types of all topics had been answered must be divided into：G=A+B+C+D+E+F.
The total score after user answers to the topic of each type and user have been obtained to all types of all topics
After total score after having answered, the total score after user answers to the topic of each type is calculated in user to all types of
Topic answer after total score in shared weighted value, respectively A%=A/G, B%=B/G, C%=C/G, D%=D/G, E%
=E/G and F%=F/G.Total score and weighted value after being answered finally according to user to the topic of each type, from data
Corresponding personality occupation type is found in storehouse, and obtains this kind of corresponding guidance instruction of personality occupation type, is joined for user
Examine, can more reasonably select the occupation of oneself.
Above-mentioned whole personality occupation type method of testing, which belongs under line, tests, and eliminates no network condition or network
The drawbacks of condition can not be tested when poor, drastically increases the portability and real-time of test.
The personality occupation type test system of another embodiment of the present invention is provided referring to Fig. 2, Fig. 2, including first obtains
Modulus block 21, the first matching module 22, the second acquisition module 23 and the second matching module 24.
First acquisition module 21, the answer type for obtaining user's selection, the answer type includes a variety of；
First matching module 22, for finding multiple topics with the answer type matching from the exam pool of database,
So that user is answered；
Second acquisition module 23, for obtaining the answer that user answers to each topic by speech recognition technology, and
Scored according to answer；
Second matching module 24, for the score according to each topic, is obtained after user answered all types topic
Total score, and corresponding personality occupational group is matched according to total score.
Referring to Fig. 3, wherein the first acquisition module 21 includes setting unit 211 and detection unit 212.
Setting unit 211, for setting corresponding glide direction for each answer type；
Detection unit 212, the glide direction for detecting user, and according to the glide direction, obtain corresponding answer
Type.
Referring to Fig. 4, wherein, the second acquisition module 23 includes collecting unit 231, filter unit 232 and computing unit 233.
Collecting unit 231, for gathering audio-frequency information when user answers to each topic；Filter unit 232, is used for
Processing is filtered to the audio-frequency information of the user of collection using Wiener Filter Method.
Computing unit 232, will be with for the audio-frequency information of user and multiple standard audio informations to be carried out into Similarity Measure
The corresponding topic answer of similarity highest standard audio information of the audio-frequency information of user as user answer, wherein, institute
The answer that standard audio information is stated with each topic is corresponding.
A kind of personality occupation type method of testing and system that the present invention is provided, in the selection course of answer type, are adopted
The mode selected with glide direction, adds the interest of whole answer process；In addition, with reference to the own situation of user, for
The topic that family provides respective type is answered, and during user's answer, user is obtained to every using speech recognition technology
The answer of answering of one of topic, and then the fraction of user's answer is drawn according to answer of answering, user's correspondence is gone out according to fractional matching
Personality type so that user refers to that scoring and personality occupation type are more clear cognitive to arrive itself personality type, is easy to close
Reason selects the occupation of oneself.
Finally, the present processes are only preferably embodiment, are not intended to limit the scope of the present invention.It is all
Within the spirit and principles in the present invention, any modification, equivalent substitution and improvements made etc. should be included in the protection of the present invention
Within the scope of.
Claims (10)
1. a kind of personality occupation type method of testing, it is characterised in that including：
S1, obtains the answer type of user's selection, and the answer type includes a variety of；
S2, finds multiple topics with the answer type matching, so that user is answered from the exam pool of database；
S3, obtains the answer that user answers to each topic, and scored according to answer by speech recognition technology；
S4, according to the score of each topic, obtains the total score after user is answered all types topic, and according to total score
Match corresponding personality occupational group.
2. personality occupation type method of testing as claimed in claim 1, it is characterised in that the step S1 is specifically included：
For each answer type, corresponding glide direction is set；
The glide direction of user is detected, and according to the glide direction, obtains corresponding answer type.
3. personality occupation type method of testing as claimed in claim 2, it is characterised in that the step S3 is specifically included：
S31, audio-frequency information when collection user answers to each topic；
S32, Similarity Measure is carried out by the audio-frequency information of user and multiple standard audio informations, wherein, the standard audio letter
Breath is corresponding with the answer of each topic；
S33, regard topic answer corresponding with the similarity highest standard audio information of the audio-frequency information of user as user's
Answer.
4. personality occupation type method of testing as claimed in claim 3, it is characterised in that also include before the step S32：
Processing is filtered to the audio-frequency information of the user of collection using Wiener Filter Method.
5. personality occupation type method of testing as claimed in claim 4, it is characterised in that the step S32 is specifically included：
Audio-frequency information after filtering process is segmented, multistage sub-audio information is formed；
Each standard audio information is segmented, the standard audio information of multiple segments is formed；
The similarity between each cross-talk audio-frequency information and corresponding every a bit of standard audio information is calculated, by all phases
It is added like degree, obtains the similarity between the audio-frequency information of user and each standard audio information.
6. personality occupation type method of testing as claimed in claim 5, it is characterised in that the step S32 also includes：
Each cross-talk audio-frequency information is inputted in HMM acoustic models, multigroup characteristic vector is exported, wherein, a cross-talk audio-frequency information
One group of characteristic vector of correspondence；And,
Multigroup characteristic vector will be exported per a bit of standard audio information in input HMM acoustic models.
7. the professional method of testing of personality as claimed in claim 6, it is characterised in that each cross-talk audio-frequency information of calculating with
Similarity between corresponding every a bit of standard audio information is specifically included：
Every a bit of standard audio of the corresponding characteristic vector of each cross-talk audio-frequency information and each standard audio information is believed
Cease corresponding characteristic vector and carry out Similarity Measure.
8. a kind of personality occupation type test system, it is characterised in that including：
First acquisition module, the answer type for obtaining user's selection, the answer type includes a variety of；
First matching module, for finding multiple topics with the answer type matching from the exam pool of database, for
Answered at family；
Second acquisition module, for obtaining the answer answered to each topic of user by speech recognition technology, and according to answering
Case is scored；
Second matching module, for the score according to each topic, obtain after user is answered all types topic must
Point, and corresponding personality occupational group is matched according to total score.
9. personality occupation type test system as claimed in claim 8, it is characterised in that first acquisition module includes：
Setting unit, for setting corresponding glide direction for each answer type；
Detection unit, the glide direction for detecting user, and according to the glide direction, obtain corresponding answer type.
10. personality occupation type test system as claimed in claim 9, it is characterised in that second acquisition module includes：
Collecting unit, for gathering audio-frequency information when user answers to each topic；
Computing unit, for the audio-frequency information of user and multiple standard audio informations to be carried out into Similarity Measure, by with user's
The corresponding topic answer of similarity highest standard audio information of audio-frequency information as user answer, wherein, the standard
Audio-frequency information is corresponding with the answer of each topic.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710398626.6A CN107239897A (en) | 2017-05-31 | 2017-05-31 | A kind of personality occupation type method of testing and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710398626.6A CN107239897A (en) | 2017-05-31 | 2017-05-31 | A kind of personality occupation type method of testing and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107239897A true CN107239897A (en) | 2017-10-10 |
Family
ID=59985314
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710398626.6A Pending CN107239897A (en) | 2017-05-31 | 2017-05-31 | A kind of personality occupation type method of testing and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107239897A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108039081A (en) * | 2017-12-22 | 2018-05-15 | 四川文理学院 | Robot teaching's assessment method and device |
| CN108647294A (en) * | 2018-05-07 | 2018-10-12 | 韦玮 | Information recommendation system |
| CN109118070A (en) * | 2018-07-28 | 2019-01-01 | 深圳微盐传媒科技有限公司 | test method and device |
| CN112070389A (en) * | 2020-09-04 | 2020-12-11 | 广州景瑞智能科技有限公司 | Method and system for matching working posts based on questionnaire survey results |
| CN112967438A (en) * | 2021-03-23 | 2021-06-15 | 函谷数巢品牌管理(广州)有限公司 | Network voting processing method and device |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1739451A (en) * | 2005-07-21 | 2006-03-01 | 高春平 | Method and device for monitoring psycological and professional test truth |
| US20080071746A1 (en) * | 2006-09-14 | 2008-03-20 | David Joseph Concordia | Method For Interactive Employment Searching, Rating, And Selecting of Employment Listing |
| US20130346332A1 (en) * | 2007-05-11 | 2013-12-26 | Agero Connected Services, Inc. | Multi-Modal Automation for Human Interactive Skill Assessment |
| CN103928023A (en) * | 2014-04-29 | 2014-07-16 | 广东外语外贸大学 | Voice scoring method and system |
| CN104008453A (en) * | 2014-05-29 | 2014-08-27 | 启秀科技（北京）有限公司 | Vocational ability evaluation simulation system |
| CN105184520A (en) * | 2015-10-22 | 2015-12-23 | 成都往来教育科技有限公司 | Evaluation method and device for professional abilities of teachers |
| CN106096289A (en) * | 2016-06-16 | 2016-11-09 | 上海工程技术大学 | A kind of personality analysis method of urban track traffic driver |
| CN106448663A (en) * | 2016-10-17 | 2017-02-22 | 海信集团有限公司 | Voice wakeup method and voice interaction device |
-
2017
- 2017-05-31 CN CN201710398626.6A patent/CN107239897A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1739451A (en) * | 2005-07-21 | 2006-03-01 | 高春平 | Method and device for monitoring psycological and professional test truth |
| US20080071746A1 (en) * | 2006-09-14 | 2008-03-20 | David Joseph Concordia | Method For Interactive Employment Searching, Rating, And Selecting of Employment Listing |
| US20130346332A1 (en) * | 2007-05-11 | 2013-12-26 | Agero Connected Services, Inc. | Multi-Modal Automation for Human Interactive Skill Assessment |
| CN103928023A (en) * | 2014-04-29 | 2014-07-16 | 广东外语外贸大学 | Voice scoring method and system |
| CN104008453A (en) * | 2014-05-29 | 2014-08-27 | 启秀科技（北京）有限公司 | Vocational ability evaluation simulation system |
| CN105184520A (en) * | 2015-10-22 | 2015-12-23 | 成都往来教育科技有限公司 | Evaluation method and device for professional abilities of teachers |
| CN106096289A (en) * | 2016-06-16 | 2016-11-09 | 上海工程技术大学 | A kind of personality analysis method of urban track traffic driver |
| CN106448663A (en) * | 2016-10-17 | 2017-02-22 | 海信集团有限公司 | Voice wakeup method and voice interaction device |
Non-Patent Citations (1)
| Title |
|---|
| 滕海坤等: "基于语音识别技术的英语发音评测系统研究", 《盐城工学院学报（自然科学版）》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108039081A (en) * | 2017-12-22 | 2018-05-15 | 四川文理学院 | Robot teaching's assessment method and device |
| CN108647294A (en) * | 2018-05-07 | 2018-10-12 | 韦玮 | Information recommendation system |
| CN109118070A (en) * | 2018-07-28 | 2019-01-01 | 深圳微盐传媒科技有限公司 | test method and device |
| CN112070389A (en) * | 2020-09-04 | 2020-12-11 | 广州景瑞智能科技有限公司 | Method and system for matching working posts based on questionnaire survey results |
| CN112967438A (en) * | 2021-03-23 | 2021-06-15 | 函谷数巢品牌管理(广州)有限公司 | Network voting processing method and device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107239897A (en) | A kind of personality occupation type method of testing and system | |
| US11037553B2 (en) | Learning-type interactive device | |
| CN106847260B (en) | Automatic English spoken language scoring method based on feature fusion | |
| CN108711436A (en) | Speaker verification's system Replay Attack detection method based on high frequency and bottleneck characteristic | |
| CN106611604A (en) | An automatic voice summation tone detection method based on a deep neural network | |
| CN104427109B (en) | Method for establishing contact item by voices and electronic equipment | |
| CN109192224A (en) | A kind of speech evaluating method, device, equipment and readable storage medium storing program for executing | |
| CN109462603A (en) | Voiceprint authentication method, equipment, storage medium and device based on blind Detecting | |
| CN109739354A (en) | A kind of multimedia interaction method and device based on sound | |
| CN111312286A (en) | Age identification method, age identification device, age identification equipment and computer readable storage medium | |
| CN109473102A (en) | A kind of robot secretary intelligent meeting recording method and system | |
| US20210320997A1 (en) | Information processing device, information processing method, and information processing program | |
| CN107886968A (en) | Speech evaluating method and system | |
| CN106971724A (en) | A kind of anti-tampering method for recognizing sound-groove and system | |
| Morrison et al. | Introduction to forensic voice comparison | |
| CN109785846A (en) | The role recognition method and device of the voice data of monophonic | |
| CN109671309A (en) | A kind of mistake pronunciation recognition methods and electronic equipment | |
| CN109410935A (en) | A kind of destination searching method and device based on speech recognition | |
| CN107195312B (en) | Method and device for determining emotion releasing mode, terminal equipment and storage medium | |
| CN104049869B (en) | A kind of data processing method and device | |
| CN109065024A (en) | abnormal voice data detection method and device | |
| CN111145726A (en) | Deep learning-based sound scene classification method, system, device and storage medium | |
| CN106445654B (en) | Determine the method and device of responsing control command priority | |
| CN107256455A (en) | A kind of career planning method of testing and system | |
| CN111862991A (en) | Method and system for identifying baby crying |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20171010 |
|
| RJ01 | Rejection of invention patent application after publication |