CN106157135A - Antifraud system and method based on Application on Voiceprint Recognition Sex, Age - Google Patents
Antifraud system and method based on Application on Voiceprint Recognition Sex, Age Download PDFInfo
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- CN106157135A CN106157135A CN201610551579.XA CN201610551579A CN106157135A CN 106157135 A CN106157135 A CN 106157135A CN 201610551579 A CN201610551579 A CN 201610551579A CN 106157135 A CN106157135 A CN 106157135A
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Classifications
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- 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
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/03—Credit; Loans; Processing thereof
-
- 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
- G06Q20/00—Payment architectures, schemes or protocols
- G06Q20/38—Payment protocols; Details thereof
- G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
- G06Q20/401—Transaction verification
- G06Q20/4014—Identity check for transactions
- G06Q20/40145—Biometric identity checks
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/02—Feature extraction for speech recognition; Selection of recognition unit
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; 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 OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/08—Speech classification or search
Abstract
The invention discloses a kind of antifraud system and method based on Application on Voiceprint Recognition Sex, Age, this system includes speech data collection module, mfcc characteristic extracting module, cluster labels characteristic extracting module based on Kmeans, supervised learning sorter model module based on GBDT, model prediction module, inspection module, identifying data submits module to, probability of cheating computing module, speech data collection module, mfcc characteristic extracting module, cluster labels characteristic extracting module based on Kmeans, supervised learning sorter model module based on GBDT, model prediction module, inspection module, probability of cheating computing module is sequentially connected with, identifying data submits to module to be connected with inspection module.The present invention can pass through fingerprint recognition, remotely verifies the true identity of debtor, reaches the anti-effect swindled, and protects the legitimate rights and interests of consumers and safeguards network service safety.
Description
Technical field
The present invention relates to a kind of algorithmic system and method, particularly relate to a kind of based on Application on Voiceprint Recognition Sex, Age anti-take advantage of
Swindleness system and method.
Background technology
Modern network technology is more and more flourishing, and network service is the most diversified, and the especially credit of network automatically is borrowed money,
But this service, it is difficult to remotely verify the true identity of debtor, because borrower is possibly through forging debt-credit
Data (application material such as photo) or steal other people data gain loan by cheating.
Summary of the invention
The technical problem to be solved be to provide a kind of antifraud system based on Application on Voiceprint Recognition Sex, Age and
Method, it can pass through fingerprint recognition, remotely verify the true identity of debtor, reach the anti-effect swindled.
The present invention solves above-mentioned technical problem by following technical proposals: a kind of based on Application on Voiceprint Recognition Sex, Age
Antifraud system, it includes speech data collection module, mfcc characteristic extracting module, cluster labels feature based on Kmeans
Extraction module, supervised learning sorter model module based on GBDT, model prediction module, inspection module, identifying data carry
Hand over module, probability of cheating computing module, speech data collection module, mfcc characteristic extracting module, cluster mark based on Kmeans
Sign characteristic extracting module, supervised learning sorter model module based on GBDT, model prediction module, inspection module, swindle
Probability evaluation entity is sequentially connected with, and identifying data submits to module to be connected with inspection module.
Preferably, described speech data collection module passes through cloud calling system real-time collecting borrower's speech data.
Preferably, the result that system is submitted to by described inspection module cross-checks with the result of prediction, and will inspection
Result is sent to probability of cheating computing module.
Preferably, the data that speech data collection module is collected by described mfcc characteristic extracting module resolve, and go forward side by side
Row mfcc feature extraction.
Preferably, the described cluster labels characteristic extracting module based on the Kmeans clustering method by Kmeans, by number
After classification, the feature of the lower data of the cluster of research difference respectively.
Preferably, described supervised learning sorter model module based on GBDT utilizes mark sample be trained and divide
Class.
Preferably, the verity at target gender and age, by the analysis to data, is carried out by described model prediction module
Judge, obtain its corresponding probability.
Preferably, described identifying data submits to module for collecting the body that borrower is submitted to automatically by network loaning bill platform
Part data.
Preferably, described probability of cheating computing module is used for by the result of crosscheck is carried out data analysis, and counts
Calculate the probability of borrower's swindle.
The present invention also provides for a kind of antifraud method based on Application on Voiceprint Recognition Sex, Age, and it comprises the following steps:
Step one: borrower submits identifying data to voluntarily by network loaning bill platform, and is borrowed by cloud calling system real-time collecting
Money people's speech data；
Step 2: mfcc feature extraction is carried out for speech data；
Step 3: cluster labels feature extraction based on Kmeans；
Step 4: by supervised learning sorter model based on GBDT data be trained and classify；
Step 5: submission result is cross-checked with predicting the outcome；
Step 6: calculate the probability of borrower's swindle.
Preferably, described mfcc feature extraction is compared than LPCC based on channel model has more preferable Shandong nation property, more accords with
Close the auditory properties of human ear, and still there is when signal to noise ratio reduces preferable recognition performance.
Preferably, described step 4 is by setting up a pattern in training data, and pattern speculates new according to this
Instances, training data is made up of input object and expection output, and the output of function is a continuous print value, or
Predict a tag along sort.
Preferably, if the probability that described step 6 finally draws is more than 50%, then swindle it is judged to；If probability less than 50% and
More than 5%, then return step one and again collect speech data；If probability is less than 5%, then it is judged to non-swindle.
The most progressive effect of the present invention is: the present invention can pass through fingerprint recognition, the remotely true body to debtor
Part is verified, reaches the anti-effect swindled, protects the legitimate rights and interests of consumers and safeguard network service safety.
Accompanying drawing explanation
Fig. 1 is the system construction drawing of the present invention.
Fig. 2 is the system flow chart of the present invention.
Detailed description of the invention
Below in conjunction with specific embodiment, the present invention is described in detail.Following example will assist in the technology of this area
Personnel are further appreciated by the present invention, but limit the present invention the most in any form.It should be pointed out that, the ordinary skill to this area
For personnel, without departing from the inventive concept of the premise, it is also possible to make some deformation and improvement.These broadly fall into the present invention
Protection domain.
As shown in Figure 1 to Figure 2, present invention antifraud based on Application on Voiceprint Recognition Sex, Age system includes that speech data is collected
Module 1, mfcc characteristic extracting module 2, cluster labels characteristic extracting module 3 based on Kmeans, based on GBDT have supervision learn
Practise sorter model module 4, model prediction module 5, inspection module 6, identifying data submission module 7, probability of cheating computing module
8, speech data collection module 1, mfcc characteristic extracting module 2, cluster labels characteristic extracting module 3 based on Kmeans, based on
The supervised learning sorter model module 4 of GBDT, model prediction module 5, inspection module 6, probability of cheating computing module 8 are successively
Connecting, identifying data submits to module 7 to be connected with inspection module 6.
Speech data collection module 1 is by cloud calling system real-time collecting borrower's speech data.
The data that speech data collection module 1 is collected by mfcc characteristic extracting module 2 resolve, and carry out mfcc feature
Extract.
Cluster labels characteristic extracting module of based on Kmeans 3 is by the clustering method of Kmeans, after sorting data into, point
Different Yan Jiu not cluster the feature of lower data.
Supervised learning sorter model module 4 based on GBDT utilizes mark sample be trained and classify.
The verity at target gender and age, by the analysis to data, is judged, obtains it by model prediction module 5
Corresponding probability.
The result that system is submitted to by inspection module 6 cross-checks with the result of prediction, and assay is sent to
Probability of cheating computing module 8.
Identifying data submits to module 7 for collecting the identifying data that borrower is submitted to automatically by network loaning bill platform.
Probability of cheating computing module 8 is used for by the result of crosscheck is carried out data analysis, and calculates borrower
The probability of swindle.
Present invention antifraud based on Application on Voiceprint Recognition Sex, Age method comprises the following steps:
Step one: borrower submits identifying data to voluntarily by network loaning bill platform, and is borrowed by cloud calling system real-time collecting
Money people's speech data.
Step 2: mfcc feature extraction is carried out for speech data.Mfcc feature extraction is than LPCC based on channel model
Compare and there is more preferable Shandong nation property, more meet the auditory properties of human ear, and still there is when signal to noise ratio reduces preferably knowledge
Other performance.
Step 3: cluster labels feature extraction based on Kmeans.It is short that this extracting method calculates the time period, and speed is fast, holds
Easily explaining, Clustering Effect is good.
Step 4: by supervised learning sorter model based on GBDT data be trained and classify.By training
Setting up a pattern in data, and pattern speculates new instances according to this, training data is by input object and expection output
Being formed, the output of function is a continuous print value, or one tag along sort of prediction.
Step 5: submission result is cross-checked with predicting the outcome.
Step 6: calculate the probability of borrower's swindle.If probability is more than 50%, then it is judged to swindle；If probability is less than 50%
And more than 5%, then return step one and again collect speech data；If probability is less than 5%, then it is judged to non-swindle.
In sum, the present invention can pass through fingerprint recognition, remotely verifies the true identity of debtor, reaches anti-
The effect of swindle, protects the legitimate rights and interests of consumers and safeguards network service safety.
Above the specific embodiment of the present invention is described.It is to be appreciated that the invention is not limited in above-mentioned
Particular implementation, those skilled in the art can make various deformation or amendment within the scope of the claims, this not shadow
Ring the flesh and blood of the present invention.
Claims (10)
1. an antifraud system based on Application on Voiceprint Recognition Sex, Age, it is characterised in that it include speech data collection module,
Mfcc characteristic extracting module, cluster labels characteristic extracting module based on Kmeans, supervised learning grader based on GBDT
Model module, model prediction module, inspection module, identifying data submit module, probability of cheating computing module to, and speech data is collected
Module, mfcc characteristic extracting module, cluster labels characteristic extracting module based on Kmeans, supervised learning based on GBDT divide
Class device model module, model prediction module, inspection module, probability of cheating computing module are sequentially connected with, and identifying data submits module to
It is connected with inspection module.
2. antifraud system based on Application on Voiceprint Recognition Sex, Age as claimed in claim 1, it is characterised in that described voice number
According to collection module by cloud calling system real-time collecting borrower's speech data.
3. antifraud system based on Application on Voiceprint Recognition Sex, Age as claimed in claim 1, it is characterised in that described inspection mould
The result that system is submitted to by block cross-checks with the result of prediction, and assay is sent to probability of cheating calculating mould
Block.
4. antifraud system based on Application on Voiceprint Recognition Sex, Age as claimed in claim 1, it is characterised in that described mfcc is special
Levy the data that speech data collection module collected by extraction module to resolve, and carry out mfcc feature extraction.
5. antifraud system based on Application on Voiceprint Recognition Sex, Age as claimed in claim 1, it is characterised in that described based on
The cluster labels characteristic extracting module of the Kmeans clustering method by Kmeans, after sorting data into, research difference respectively is poly-
The feature of data under class.
6. antifraud system based on Application on Voiceprint Recognition Sex, Age as claimed in claim 1, it is characterised in that described based on
The supervised learning sorter model module of GBDT utilizes mark sample be trained and classify.
7. antifraud system based on Application on Voiceprint Recognition Sex, Age as claimed in claim 1, it is characterised in that described model is pre-
The verity at target gender and age, by the analysis to data, is judged, obtains its corresponding probability by survey module.
8. antifraud system based on Application on Voiceprint Recognition Sex, Age as claimed in claim 1, it is characterised in that described identity provides
Material submits to module for collecting the identifying data that borrower is submitted to automatically by network loaning bill platform.
9. antifraud system based on Application on Voiceprint Recognition Sex, Age as claimed in claim 1, it is characterised in that described swindle is general
Rate computing module is used for by the result of crosscheck carries out data analysis, and calculates the probability of borrower's swindle.
10. an antifraud method based on Application on Voiceprint Recognition Sex, Age, it is characterised in that it comprises the following steps:
Step one: borrower submits identifying data to voluntarily by network loaning bill platform, and is borrowed by cloud calling system real-time collecting
Money people's speech data；
Step 2: mfcc feature extraction is carried out for speech data；
Step 3: cluster labels feature extraction based on Kmeans；
Step 4: by supervised learning sorter model based on GBDT data be trained and classify；
Step 5: submission result is cross-checked with predicting the outcome；
Step 6: calculate the probability of borrower's swindle.
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107358945A (en) * | 2017-07-26 | 2017-11-17 | 谢兵 | A kind of more people's conversation audio recognition methods and system based on machine learning |
| CN107464115A (en) * | 2017-07-20 | 2017-12-12 | 北京小米移动软件有限公司 | personal characteristic information verification method and device |
| CN107680602A (en) * | 2017-08-24 | 2018-02-09 | 平安科技（深圳）有限公司 | Voice fraud recognition methods, device, terminal device and storage medium |
| CN107919137A (en) * | 2017-10-25 | 2018-04-17 | 平安普惠企业管理有限公司 | The long-range measures and procedures for the examination and approval, device, equipment and readable storage medium storing program for executing |
| CN108053838A (en) * | 2017-12-01 | 2018-05-18 | 上海壹账通金融科技有限公司 | With reference to audio analysis and fraud recognition methods, device and the storage medium of video analysis |
| CN108732559A (en) * | 2018-03-30 | 2018-11-02 | 北京邮电大学 | A kind of localization method, device, electronic equipment and readable storage medium storing program for executing |
| CN110084224A (en) * | 2019-05-08 | 2019-08-02 | 电子科技大学 | Finger print safety Verification System and method on a kind of cloud |
| CN110276679A (en) * | 2019-05-23 | 2019-09-24 | 武汉大学 | A kind of network individual credit fraud detection method towards deep learning |
| CN110648670A (en) * | 2019-10-22 | 2020-01-03 | 中信银行股份有限公司 | Fraud identification method and device, electronic equipment and computer-readable storage medium |
| CN111683181A (en) * | 2020-04-27 | 2020-09-18 | 平安科技（深圳）有限公司 | Voice-based user gender and age identification method and device and computer equipment |
| CN111861487A (en) * | 2020-07-10 | 2020-10-30 | 中国建设银行股份有限公司 | Financial transaction data processing method, and fraud monitoring method and device |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102238190A (en) * | 2011-08-01 | 2011-11-09 | 安徽科大讯飞信息科技股份有限公司 | Identity authentication method and system |
| CN104200814A (en) * | 2014-08-15 | 2014-12-10 | 浙江大学 | Speech emotion recognition method based on semantic cells |
| CN104700843A (en) * | 2015-02-05 | 2015-06-10 | 海信集团有限公司 | Method and device for identifying ages |
| CN105357006A (en) * | 2014-08-20 | 2016-02-24 | 中兴通讯股份有限公司 | Method and equipment for performing security authentication based on voiceprint feature |
-
2016
- 2016-07-14 CN CN201610551579.XA patent/CN106157135A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102238190A (en) * | 2011-08-01 | 2011-11-09 | 安徽科大讯飞信息科技股份有限公司 | Identity authentication method and system |
| CN104200814A (en) * | 2014-08-15 | 2014-12-10 | 浙江大学 | Speech emotion recognition method based on semantic cells |
| CN105357006A (en) * | 2014-08-20 | 2016-02-24 | 中兴通讯股份有限公司 | Method and equipment for performing security authentication based on voiceprint feature |
| CN104700843A (en) * | 2015-02-05 | 2015-06-10 | 海信集团有限公司 | Method and device for identifying ages |
Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107464115A (en) * | 2017-07-20 | 2017-12-12 | 北京小米移动软件有限公司 | personal characteristic information verification method and device |
| CN107358945A (en) * | 2017-07-26 | 2017-11-17 | 谢兵 | A kind of more people's conversation audio recognition methods and system based on machine learning |
| CN107680602A (en) * | 2017-08-24 | 2018-02-09 | 平安科技（深圳）有限公司 | Voice fraud recognition methods, device, terminal device and storage medium |
| CN107919137A (en) * | 2017-10-25 | 2018-04-17 | 平安普惠企业管理有限公司 | The long-range measures and procedures for the examination and approval, device, equipment and readable storage medium storing program for executing |
| CN108053838A (en) * | 2017-12-01 | 2018-05-18 | 上海壹账通金融科技有限公司 | With reference to audio analysis and fraud recognition methods, device and the storage medium of video analysis |
| WO2019104890A1 (en) * | 2017-12-01 | 2019-06-06 | 深圳壹账通智能科技有限公司 | Fraud identification method and device combining audio analysis and video analysis and storage medium |
| CN108053838B (en) * | 2017-12-01 | 2019-10-11 | 深圳壹账通智能科技有限公司 | In conjunction with fraud recognition methods, device and the storage medium of audio analysis and video analysis |
| CN108732559B (en) * | 2018-03-30 | 2021-09-24 | 北京邮电大学 | Positioning method, positioning device, electronic equipment and readable storage medium |
| CN108732559A (en) * | 2018-03-30 | 2018-11-02 | 北京邮电大学 | A kind of localization method, device, electronic equipment and readable storage medium storing program for executing |
| CN110084224A (en) * | 2019-05-08 | 2019-08-02 | 电子科技大学 | Finger print safety Verification System and method on a kind of cloud |
| CN110084224B (en) * | 2019-05-08 | 2022-08-05 | 电子科技大学 | Cloud fingerprint security authentication system and method |
| CN110276679A (en) * | 2019-05-23 | 2019-09-24 | 武汉大学 | A kind of network individual credit fraud detection method towards deep learning |
| CN110276679B (en) * | 2019-05-23 | 2021-05-04 | 武汉大学 | Network personal credit fraud behavior detection method for deep learning |
| CN110648670B (en) * | 2019-10-22 | 2021-11-26 | 中信银行股份有限公司 | Fraud identification method and device, electronic equipment and computer-readable storage medium |
| CN110648670A (en) * | 2019-10-22 | 2020-01-03 | 中信银行股份有限公司 | Fraud identification method and device, electronic equipment and computer-readable storage medium |
| CN111683181A (en) * | 2020-04-27 | 2020-09-18 | 平安科技（深圳）有限公司 | Voice-based user gender and age identification method and device and computer equipment |
| CN111683181B (en) * | 2020-04-27 | 2022-04-12 | 平安科技（深圳）有限公司 | Voice-based user gender and age identification method and device and computer equipment |
| CN111861487A (en) * | 2020-07-10 | 2020-10-30 | 中国建设银行股份有限公司 | Financial transaction data processing method, and fraud monitoring method and device |
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