CN113393318A - Bank card application wind control method and device, electronic equipment and medium - Google Patents

Bank card application wind control method and device, electronic equipment and medium Download PDF

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CN113393318A
CN113393318A CN202110650576.2A CN202110650576A CN113393318A CN 113393318 A CN113393318 A CN 113393318A CN 202110650576 A CN202110650576 A CN 202110650576A CN 113393318 A CN113393318 A CN 113393318A
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黎明鸣
赵阳
柳毅
戴云飞
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Industrial and Commercial Bank of China Ltd ICBC
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Industrial and Commercial Bank of China Ltd ICBC
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Abstract

The disclosure provides a bank card application wind control method based on voiceprint recognition, a bank card application wind control device, electronic equipment, a computer readable storage medium and a computer program. The bank card application wind control method and device can be used in the technical field of artificial intelligence. The bank card application wind control method comprises the following steps: receiving a bank card application request of a user; when a user applies for a bank card, acquiring first voiceprint information of the user; when a user is investigated, second voiceprint information of the user is obtained; determining a first similarity of the first voiceprint information and the second voiceprint information; when the first similarity is larger than a first threshold value, determining a second similarity between the second voiceprint information and the black voiceprint in the black voiceprint library; and when the second similarity is larger than a second threshold value, storing the first voiceprint information and/or the second voiceprint information into a black voiceprint library.

Description

Bank card application wind control method and device, electronic equipment and medium
Technical Field
The present disclosure relates to the field of artificial intelligence technologies, and in particular, to a method and an apparatus for applying for a bank card and a computer program product for applying for a bank card based on voiceprint recognition.
Background
The credit card application risk screening is an indispensable part of bank risk control, the related risk strategies can reduce the risk of open accounts and bad accounts of banks, good risk screening evasive measures can improve the credit of the banks and reduce the bad assets of the banks, and the traditional credit card application risk prevention and control mechanism is mainly based on the information elements filled by the applicant and combines each system inside and outside the banks to identify the identity of a customer. For example, according to the information such as the telephone number and the ID card number filled in when the client applies for the application, the client application information is ensured to be consistent with the external system registration information through the networking check of the public security department, the telephone information check of the operator and the like. And the authenticity of the client can be rechecked in a manual investigation mode.
Disclosure of Invention
In view of the above, the present disclosure provides a method for banking card application wind control based on voiceprint recognition, a device for banking card application wind control, an electronic device, a computer-readable storage medium, and a computer program product, which are beneficial to risk control and reduce economic loss.
One aspect of the present disclosure provides a bank card application wind control method based on voiceprint recognition, including: receiving a bank card application request of a user; when a user applies for a bank card, acquiring first voiceprint information of the user; when a user is investigated, second voiceprint information of the user is obtained; determining a first similarity of the first voiceprint information and the second voiceprint information; when the first similarity is larger than a first threshold value, determining a second similarity between the second voiceprint information and the black voiceprint in the black voiceprint library; and when the second similarity is larger than a second threshold value, storing the first voiceprint information and/or the second voiceprint information to the black voiceprint library.
According to the bank card application wind control method based on voiceprint recognition, whether an actual application user and an investigated user of a bank card are the same person or not can be accurately recognized according to comparison of the first voiceprint information and the second voiceprint information; and whether the bank card applicant has a bad record or not can be determined by comparing the black voiceprint in the black voiceprint library with the second voiceprint information, and further measures can be taken for the applicant having the bad record. The process of storing the first voiceprint information and/or the second voiceprint information in the black voiceprint library can continuously perfect the black voiceprint library. Therefore, the method of the present disclosure facilitates bank risk control, so that economic losses of banks and customers can be reduced.
In some embodiments, when the first similarity is less than the first threshold, the first voiceprint information and/or the second voiceprint information is stored to the black voiceprint library.
In some embodiments, the method further comprises: after the first voiceprint information of the user is obtained, storing the first voiceprint information into a temporary voiceprint library, wherein the temporary voiceprint library comprises at least one temporary voiceprint; the determining a first similarity of the first voiceprint information and the second voiceprint information comprises: matching the second voiceprint information to the temporary voiceprint in the temporary voiceprint repository; acquiring a similarity value list of the second voiceprint information and the temporary voiceprint; and acquiring the first similarity of the second voiceprint information and the first voiceprint information.
In some embodiments, the determining the second similarity of the second voiceprint information to a black voiceprint in a black voiceprint library comprises: acquiring a similarity value list of the second voiceprint information and the black voiceprint; and acquiring the second similarity of the second voiceprint information and the black voiceprint.
In some embodiments, the obtaining the first voiceprint information of the user includes: displaying the text information; recording voice prints of the text information read by the user; and denoising the voiceprint, wherein the voiceprint subjected to denoising is the first voiceprint information.
In some embodiments, the obtaining the first voiceprint information of the user includes: displaying the text information; recording voice prints of the text information read by the user; denoising the voiceprint, wherein denoising the voiceprint comprises removing noise irrelevant to the reading of the text information in the voiceprint to obtain a first effective voiceprint; and extracting the voiceprint feature of the first effective voiceprint, wherein the voiceprint feature is the first voiceprint information.
In some embodiments, the obtaining the first voiceprint information of the user further includes setting a first effective voiceprint progress bar, where the first effective voiceprint progress bar is a duration of the first effective voiceprint, and after the voiceprint is recorded through noise reduction processing, if the first effective voiceprint progress bar does not satisfy a threshold, the voiceprint of the text information read aloud by the user is continuously recorded, and the noise reduction processing is performed on the voiceprint until the first effective voiceprint progress bar satisfies the threshold, and the extracting the voiceprint feature of the first effective voiceprint is extracting the voiceprint feature of the first effective voiceprint after the first effective voiceprint progress bar satisfies the threshold.
In some embodiments, said extracting the voiceprint features of the first valid voiceprint comprises extracting the voiceprint features using a neural network model.
In some embodiments, the neural network model is a gaussian mixture model-a generic background model.
In some embodiments, the obtaining second voiceprint information of the user comprises: the bank dials the contact information filled in when initiating the bank card application request; and collecting audio information of the listener, wherein the audio information is the second voiceprint information.
In some embodiments, the obtaining second voiceprint information of the user comprises: the bank dials the contact information filled in when initiating the bank card application request; collecting audio information of a listener; denoising the audio information, wherein denoising the audio information comprises removing noise irrelevant to conversation content in the audio information to obtain a second effective voiceprint; and extracting the voiceprint feature of the second valid voiceprint, wherein the voiceprint feature is the second voiceprint information.
Another aspect of the present disclosure provides a bank card application wind control device based on voiceprint recognition, including: the bank card application request module is used for executing and receiving a bank card application request of a user; the first voiceprint acquisition module is used for acquiring first voiceprint information of a user when the user applies for a bank card; the second acoustic line acquisition module is used for acquiring second acoustic line information of the user when the user is investigated; the first information matching module is used for determining the first similarity of the first voiceprint information and the second voiceprint information; the second information matching module is used for determining a second similarity between the second voiceprint information and the black voiceprint in the black voiceprint library when the first similarity is larger than a first threshold; and a black voiceprint adding module, configured to store the first voiceprint information and/or the second voiceprint information in the black voiceprint library when the second similarity is greater than a second threshold.
Another aspect of the present disclosure provides an electronic device comprising one or more processors and one or more memories, wherein the memories are configured to store executable instructions that, when executed by the processors, implement the method as described above.
Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions for implementing the method as described above when executed.
Another aspect of the disclosure provides a computer program product comprising one or more executable instructions that when executed by a processor implement the method as described above
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The above and other objects, features and advantages of the present disclosure will become more apparent from the following description of embodiments of the present disclosure with reference to the accompanying drawings, in which:
fig. 1 schematically illustrates an exemplary system architecture to which the methods, apparatus, and methods may be applied, in accordance with an embodiment of the present disclosure;
fig. 2 schematically shows a flowchart of a voiceprint recognition based banking card application wind control method according to an embodiment of the present disclosure;
FIG. 3 schematically illustrates a flow chart for obtaining first voiceprint information of a user according to one embodiment of the present disclosure;
FIG. 4 schematically illustrates a flow chart for obtaining first voiceprint information of a user according to another embodiment of the disclosure;
FIG. 5 schematically shows a flow chart for extracting voiceprint features of a first valid voiceprint according to one embodiment of the present disclosure;
FIG. 6 schematically shows a flowchart for obtaining second voiceprint information of a user according to one embodiment of the present disclosure;
FIG. 7 schematically illustrates a flow chart for obtaining second voiceprint information for a user according to another embodiment of the disclosure;
FIG. 8 schematically shows a flow chart for extracting voiceprint features of a second valid voiceprint according to one embodiment of the present disclosure;
FIG. 9 schematically illustrates a flow chart for determining a first similarity of first voiceprint information to second voiceprint information according to one embodiment of the present disclosure;
FIG. 10 schematically illustrates a flow chart for determining a second similarity of second voiceprint information to a black voiceprint in a black voiceprint library according to one embodiment of the disclosure;
FIG. 11 schematically illustrates a block diagram of a bank card application wind control apparatus according to an embodiment of the present disclosure;
FIG. 12 schematically shows a block diagram of an electronic device according to an embodiment of the disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
Where a convention analogous to "A, B or at least one of C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B or C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). The terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first", "second", may explicitly or implicitly include one or more of the described features.
The credit card application risk screening is an indispensable part of bank risk control, related risk strategies can reduce the risk of bad accounts and bad accounts of banks, good risk screening evasive measures can improve the credit of the banks and reduce the bad assets of the banks, and a traditional credit card application risk prevention and control mechanism is mainly used for identifying the identity of a client by combining internal and external systems of the banks on the basis of application information elements filled by an applicant.
For example, according to the information such as the telephone number and the ID card number filled in when the client applies for the application, the client application information is ensured to be consistent with the external system registration information through the networking check of the public security department, the telephone information check of the operator and the like. And the authenticity of the client can be rechecked in a manual investigation mode. However, with the social development, the intermediary industry starts to intervene in various agent services, the internet verification of the public security department and the telephone information verification of the operator can be avoided through measures such as obtaining the identity card of the customer, manual investigation can only check the authenticity of the customer according to the system display information, and whether the actual application customer and the person to be investigated are the same person cannot be accurately identified, so that the bank risk control is not facilitated.
The embodiment of the disclosure provides a bank card application wind control method based on voiceprint recognition, a bank card application wind control device, electronic equipment, a computer readable storage medium and a computer program product. The bank card application wind control method comprises the steps of receiving a bank card application request of a user; when a user applies for a bank card, acquiring first voiceprint information of the user; when a user is investigated, second voiceprint information of the user is obtained; determining a first similarity of the first voiceprint information and the second voiceprint information; when the first similarity is larger than a first threshold value, determining a second similarity between the second voiceprint information and the black voiceprint in the black voiceprint library; and when the second similarity is larger than a second threshold value, storing the first voiceprint information and/or the second voiceprint information into a black voiceprint library.
It should be noted that the bank card application wind control method and the bank card application wind control device 10 based on voiceprint recognition in the present disclosure may be used in the field of artificial intelligence, and may also be used in any fields other than the field of artificial intelligence, such as the financial field, and the field of the present disclosure is not limited herein.
Fig. 1 schematically illustrates an exemplary system architecture 100 to which a voiceprint recognition based banking card application wind control method, a banking card application wind control apparatus, an electronic device, a computer-readable storage medium, and a computer program product may be applied, according to embodiments of the present disclosure. It should be noted that fig. 1 is only an example of a system architecture to which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, and does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.
As shown in fig. 1, the system architecture 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have installed thereon various communication client applications, such as shopping-like applications, web browser applications, search-like applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 105 may be a server providing various services, such as a background management server (for example only) providing support for websites browsed by users using the terminal devices 101, 102, 103. The background management server may analyze and perform other processing on the received data such as the user request, and feed back a processing result (e.g., a webpage, information, or data obtained or generated according to the user request) to the terminal device.
It should be noted that the bank card application wind control method based on voiceprint recognition provided by the embodiment of the present disclosure may be generally executed by the server 105. Accordingly, the banking card application wind control device 10 provided by the embodiment of the disclosure may be generally disposed in the server 105. The bank card application wind control method based on voiceprint recognition provided by the embodiment of the disclosure can also be executed by a server or a server cluster which is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and/or the server 105. Accordingly, the bank card application wind control device 10 provided by the embodiment of the present disclosure may also be disposed in a server or a server cluster different from the server 105 and capable of communicating with the terminal devices 101, 102, 103 and/or the server 105.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
The bank card application wind control method based on voiceprint recognition of the disclosed embodiment will be described in detail through fig. 2 to 10 based on the scenario described in fig. 1.
Fig. 2 schematically shows a flowchart of a bank card application wind control method based on voiceprint recognition according to an embodiment of the present disclosure.
As shown in fig. 2, the bank card application wind control method based on voiceprint recognition of the embodiment includes operations S210 to S260.
In operation S210, a bank card application request of a user is received. For example, when a user wants to apply for a bank card, the user can submit a bank card application request through the mobile terminal, and the bank server receives the bank card application request of the user through the network. The bank can set application information to be filled in when the bank card applies for the request, such as identification number, mobile phone number, name, sex, native place, work unit and other information of the application user.
In operation S220, when the user applies for a bank card, first voiceprint information of the user is acquired. It should be explained that after the user submits the application information to be filled in the bank card application request, the first voiceprint information of the user needs to be acquired, for example, the first voiceprint information may be the voice information of the user, and the way of acquiring the voice information of the user may be to start the sound recording function of the mobile terminal or start the video recording function of the mobile terminal, and the like.
Specifically, as a possible implementation manner, as shown in fig. 3, the acquiring of the first voiceprint information of the user in operation S220 may include displaying the text information in operation S221, recording a voiceprint of the user reading the text information in operation S222, and performing noise reduction processing on the voiceprint in operation S223. The mobile terminal may display the text information to the user through the display screen, where the text information may be any text, such as a word segment designed for a bank, a celebrity, or an american transcription, but is not limited thereto. The user reads the text information according to the display content, at this time, the mobile terminal can record the voiceprint of the user, the mobile terminal can also perform noise reduction processing on the recorded voiceprint, including but not limited to deleting the voiceprint irrelevant to the text information and the like, and the voiceprint after noise reduction can be used as first voiceprint information.
As another possible implementation manner, as shown in fig. 4, the acquiring of the first voiceprint information of the user in operation S220 may include displaying the text information in operation S221, recording a voiceprint of the text information read aloud by the user in operation S222, performing noise reduction processing on the voiceprint in operation S223, and extracting a voiceprint feature of the first valid voiceprint in operation S224. The noise reduction processing of the voiceprint may include removing noise irrelevant to the reading text information in the voiceprint to obtain a first effective voiceprint; therefore, the voiceprint characteristic can be extracted from the first effective voiceprint, and the voiceprint characteristic can be used as the first voiceprint information.
Further, referring to fig. 4, obtaining the first voiceprint information of the user in operation S220 may further include setting a first effective voiceprint progress bar in operation S225, where the first effective voiceprint progress bar may be a duration of the first effective voiceprint, and after the voiceprint of the user is recorded in the noise reduction processing, if the first effective voiceprint progress bar does not satisfy the threshold, the voiceprint of the user reading aloud text information is continuously recorded and the voiceprint of the user is recorded in the noise reduction processing until the first effective voiceprint progress bar satisfies the threshold, and extracting the voiceprint feature of the first effective voiceprint is the voiceprint feature of the first effective voiceprint after the first effective voiceprint progress bar satisfies the threshold.
It should be noted that, when a user applies for a bank card, the user may be in a noisy environment, and after a voiceprint of the text information read aloud by the user is recorded for the first time, other noise in the environment may be mixed in the voiceprint; the user may also suddenly speak to the surrounding people, while reading the text message, that other text messages are irrelevant. Of course, the scene of the noise appearing in the user's voiceprint is not limited thereto, and is only exemplified here.
At this time, the recorded voiceprint needs to be denoised, the voiceprint after denoising is the first effective voiceprint, but the duration of the first effective voiceprint after noise is deleted is reduced, and then the voiceprint of the user reading text information aloud needs to be recorded continuously, and the voiceprint is denoised until the first effective voiceprint progress bar meets the threshold, that is, the duration of the first effective voiceprint meets the threshold. Based on this, setting up first voiceprint progress bar can be convenient for the duration of first effective voiceprint accords with the extraction requirement to be convenient for realize extracting the voiceprint characteristic of first effective voiceprint.
Further, as shown in fig. 5, extracting the voiceprint features of the first valid voiceprint in operation S224 may include extracting the voiceprint features using a neural network model in operation S2241. It is to be appreciated that utilizing a neural network model can facilitate extracting the voiceprint features of the first valid voiceprint.
Still further, the neural network model may be a gaussian mixture model-a generic background model. Of course, the neural network model may be other models as long as the neural network model capable of extracting the voiceprint features of the first valid voiceprint falls within the scope of the present disclosure.
In operation S230, second voiceprint information of the user is acquired while investigating the user. It should be noted that, after the bank acquires the first voiceprint information of the user, the bank may investigate the user, and specifically, as an implementable manner, as shown in fig. 6, acquiring the second voiceprint information of the user in operation S230 includes dialing the contact information, which is filled when the bank initiates a bank card application request, to the bank in operation S231 and acquiring the audio information of the listener in operation S232, where the audio information is the second voiceprint information. Here, the contact means may include, but is not limited to, a mobile phone number, a micro signal, a QQ number, and the like. After the dialing initiated by the bank is connected, the audio information of the listener can be collected.
As another practical manner, as shown in fig. 7, the operation S230 of obtaining the second voiceprint information of the user includes an operation S231 of dialing the contact information filled when the request for applying the bank card is initiated by the bank, an operation S232 of collecting the audio information of the listener, an operation S233 of denoising the audio information, and an operation S234 of extracting the voiceprint feature of the second valid voiceprint. Wherein the noise reduction processing the audio information comprises removing noise in the audio information that is not related to the dialog content to obtain a second valid voiceprint.
It should be noted that when the bank is conducting a survey, i.e., when the bank initiates a dial to the user, the user may be in a noisy environment or the user may speak something else unrelated to the conversation to the surrounding people while listening to the voice. Of course, the scene of the noise appearing in the voiceprint of the user during the survey is not limited to this, and is only exemplified here.
At this moment, the recorded audio information needs to be denoised, and the voiceprint after denoising is the second effective voiceprint, so that the voiceprint characteristics can be extracted from the second effective voiceprint, and the voiceprint characteristics can be used as second voiceprint information.
Further, as shown in fig. 8, the extracting the voiceprint features of the second valid voiceprint in operation S234 may include extracting the voiceprint features using a neural network model in operation S2341. It is to be appreciated that utilizing a neural network model can facilitate extracting a voiceprint feature of a second valid voiceprint. Still further, the neural network model may be a gaussian mixture model-a generic background model. Of course the neural network model may also be other models.
In operation S240, a first similarity of the first voiceprint information and the second voiceprint information is determined.
In operation S250, when the first similarity is greater than the first threshold, a second similarity of the second voiceprint information to the black voiceprint in the black voiceprint library is determined. It can be understood that, after the first voiceprint information and the second voiceprint information are obtained, the first voiceprint information and the second voiceprint information can be compared to determine a first similarity of the first voiceprint information and the second voiceprint information, for example, the first similarity of the first voiceprint information and the second voiceprint information can be determined through an algorithm model, a first threshold value can be implanted into the algorithm model, when the first similarity is greater than the first threshold value, it is determined that the first voiceprint information and the second voiceprint information are matched in comparison, and it can be determined that the person applies for the bank card. The first threshold value is any value that can prove that the first voiceprint information and the second voiceprint information match, for example 99.5%.
After the bank card is proved to be applied by the user, the second voiceprint information can be compared with the black voiceprint in the black voiceprint library to determine the second similarity between the second voiceprint information and the black voiceprint. Here, the black voiceprint may be understood as the voiceprint information of the application user who has a bad record, for example, the application user who has a bad record may be a user whose contact information, name, identification number, and the like, which are submitted during application, do not match with each other, or the first voiceprint information and the second voiceprint information do not match, that is, a user who is not applied by the user himself is found during bank investigation. Further, the black voiceprint library can be understood as a database storing black voiceprints.
Wherein the second similarity of the second voiceprint information to the black voiceprint can be determined, for example, by an algorithmic model, in which a second threshold can be implanted.
In operation S260, when the second similarity is greater than the second threshold, the first voiceprint information and/or the second voiceprint information is stored to the black voiceprint library. It is understood that when the second similarity is greater than the second threshold, it indicates that the second voiceprint information and the black voiceprint comparison match, and it can be determined that the applicant of the bank card has a bad record and needs to take further measures, such as further examination, and the like. For insurance, the first voiceprint information can be stored to a black voiceprint repository; the second voiceprint information can also be stored in a black voiceprint library; the first voiceprint information and the second voiceprint information can also be stored in a black voiceprint library. The second threshold is any value that can prove that the second voiceprint information matches the black voiceprint, for example 99.5%.
According to the bank card application wind control method based on voiceprint recognition, whether an actual application user and an investigated user of a bank card are the same person or not can be accurately recognized according to comparison of the first voiceprint information and the second voiceprint information; and whether the bank card applicant has a bad record or not can be determined by comparing the black voiceprint in the black voiceprint library with the second voiceprint information, and further measures can be taken for the applicant having the bad record. The process of storing the first voiceprint information and/or the second voiceprint information in the black voiceprint library can continuously perfect the black voiceprint library. Therefore, the method of the present disclosure facilitates bank risk control, so that economic losses of banks and customers can be reduced.
In some embodiments of the present disclosure, as shown in fig. 2, when the first similarity is less than the first threshold, operation 270 stores the first voiceprint information and/or the second voiceprint information to a black voiceprint library. It is understood that when the first similarity is smaller than the first threshold, it indicates that the first voiceprint information and the second voiceprint information do not coincide, it may be determined that the user does not apply for the bank card, and further measures may be taken, for example, further manually reviewing the application information and the applicant of the bank card. Meanwhile, the first voiceprint information can be stored in a black voiceprint library; the second voiceprint information can also be stored in a black voiceprint library; the first voiceprint information and the second voiceprint information can also be stored in a black voiceprint library.
In some embodiments of the present disclosure, as shown in fig. 2, when the second similarity is smaller than the second threshold, which indicates that the second voiceprint information and the black voiceprint do not match, it can be determined that the applicant has not recorded a bad record. At this time, the bank can give a survey conclusion of the bank card application wind control method mentioned in the disclosure.
Fig. 9 schematically illustrates a flowchart for determining a first similarity of first voiceprint information to second voiceprint information according to an embodiment of the disclosure. After first voiceprint information of a user is obtained, the first voiceprint information is stored in a temporary voiceprint library, and the temporary voiceprint library comprises at least one temporary voiceprint. In other words, after the first voiceprint information is stored in the temporary voiceprint library, the first voiceprint information is a temporary voiceprint in the temporary voiceprint library, and there may be other voiceprints already stored in the temporary voiceprint library.
Operation S240 determining the first similarity of the first voiceprint information to the second voiceprint information includes operations S241 through S243.
In operation S241, the second voiceprint information is matched with the temporary voiceprint in the temporary voiceprint library.
In operation S242, a similarity value list of the second voiceprint information and the temporary voiceprint is acquired. The temporary voiceprint library may have n temporary voiceprints, where n is an integer greater than or equal to 1, and the second voiceprint information may be compared with each temporary voiceprint, so that n similarity values may be obtained.
In operation S243, a first similarity of the second voiceprint information to the first voiceprint information is acquired. The first similarity between the second voiceprint information and the first voiceprint information can be inquired in the list, so that the first similarity between the second voiceprint information and the first voiceprint information can be conveniently acquired, and comparison between the first similarity and the first threshold can be further realized.
FIG. 10 schematically illustrates a flow chart for determining a second similarity of second voiceprint information to a black voiceprint in a black voiceprint library according to an embodiment of the disclosure.
Operation S250 determining the second similarity of the second voiceprint information to the black voiceprint in the black voiceprint library includes operations S251 to S252.
In operation S251, a similarity value list of the second voiceprint information and the black voiceprint is acquired. The method includes the steps that s black voiceprints can be stored in a black voiceprint library, s is an integer larger than or equal to 1, second voiceprint information can be compared with each black voiceprint, s similarity values can be obtained, as an example, according to the sequence from big to small, the first t similarity values can be displayed as a list, and t is an integer smaller than or equal to s.
In operation S252, a second similarity of the second voiceprint information to the black voiceprint is acquired. The second similarity between the second voiceprint information and the black voiceprint can be queried in the list, so that the second similarity between the second voiceprint information and the black voiceprint can be conveniently acquired, and comparison between the second similarity and a second threshold can be further realized.
The bank card application wind control method based on voiceprint recognition according to the embodiment of the disclosure is described in detail below. It is to be understood that the following description is illustrative only and is not intended to be in any way limiting of the present disclosure.
According to the bank card application wind control method based on voiceprint recognition, a user initiates a bank card application from a mobile terminal, and the black voiceprint library is subjected to neural network algorithms such as multiple hardware voiceprint acquisition, noise reduction processing, voiceprint model training, matching and the like, and is subjected to supplementary management by combining a bank user application bank card risk screening mechanism.
The method comprises the following steps: the user initiates a bank card application based on the mobile terminal, the mobile terminal displays text information and is read aloud by the user to acquire user audio information, the environment of the user during application is complex, noise reduction processing needs to be carried out on the effective audio, clear and available voiceprint streams are acquired, a front end is returned to acquire an effective voiceprint progress bar, voiceprint recording can be interrupted after the progress bar is full, and effective voiceprint judgment time is flexibly configured through parameters.
Step two: extracting the voiceprint characteristics based on the voiceprint acquisition equipment and the neural network model, and training the neural network model to generate model data. And training a Gaussian mixture model-universal background model (GMM-UBM) to obtain the voiceprint characteristics of the user and registering the voiceprint characteristics to a temporary voiceprint library, wherein the Gaussian mixture model-universal background model (GMM-UBM) is a voiceprint model trained on the basis of mass background data, and the extracted voiceprint characteristics are registered to the temporary voiceprint library.
Step three: the method comprises the steps that a bank investigator initiates investigation, dials out an application telephone number, initiates investigation, repeats step one, takes voiceprint information of the investigated person, initiates first voiceprint matching by using a neural network algorithm, carries out consistency matching on the investigated voiceprint and a temporary voiceprint library, and returns a former N similarity score voiceprint list (N values and similarity thresholds of the former N can be adjusted by parameters), wherein N is an integer larger than or equal to 1, if the matching degree of the investigated person and the temporary voiceprint library is low, the authenticity of the investigated user and an application user is manually consulted, voiceprint characteristics registered in the temporary voiceprint library and/or the investigated voiceprint are added into a black voiceprint library, and the system provides a foreground function for adding a voiceprint black list for a service. And if the matching degree of the person to be checked and the temporary voiceprint library is higher, executing a step four.
Step four: and further, the voice print information of the person to be investigated is searched in the black voice print library, the former N similarity score lists are returned and displayed to the foreground, and the voice print characteristics registered in the temporary voice print library and/or the investigated voice print are transferred to the black voice print library.
Step five: and manually, flexibly operating, increasing and deleting the black voiceprint list through a system foreground, and giving a survey conclusion and continuing follow-up survey steps.
Based on the bank card application wind control method based on voiceprint recognition, the disclosure also provides a bank card application wind control device 10 based on voiceprint recognition. The bank card application wind control device 10 will be described in detail with reference to fig. 11.
Fig. 11 schematically shows a block diagram of the bank card application wind control device 10 according to the embodiment of the present disclosure.
The bank card application wind control device 10 comprises a bank card application request module 1, a first voiceprint acquisition module 2, a second voiceprint acquisition module 3, a first information matching module 4, a second information matching module 5 and a black voiceprint adding module 6.
A bank card application request module 1, configured to execute operation S210 to receive a bank card application request of a user.
The first voiceprint obtaining module 2 is configured to execute operation S220 to obtain first voiceprint information of the user when the user applies for the bank card.
The second fingerprint obtaining module 3 is configured to perform operation S230 to obtain second fingerprint information of the user when investigating the user.
The first information matching module 4 is configured to perform operation S240 to determine a first similarity between the first voiceprint information and the second voiceprint information.
The second information matching module 5 is configured to perform operation S250 to determine a second similarity between the second voiceprint information and the black voiceprint in the black voiceprint library when the first similarity is greater than the first threshold.
And a black voiceprint adding module 6, configured to execute operation S260 to store the first voiceprint information and/or the second voiceprint information in the black voiceprint library when the second similarity is greater than the second threshold.
Since the bank card application wind control device 10 is configured based on the bank card application wind control method, the beneficial effects of the bank card application wind control device 10 are the same as those of the bank card application wind control method, and are not described herein again.
In addition, according to the embodiment of the present disclosure, any multiple modules of the bank card application request module 1, the first voiceprint acquisition module 2, the second voiceprint acquisition module 3, the first information matching module 4, the second information matching module 5, and the black voiceprint adding module 6 may be combined into one module to be implemented, or any one module thereof may be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module.
According to the embodiment of the present disclosure, at least one of the bank card application request module 1, the first voiceprint obtaining module 2, the second voiceprint obtaining module 3, the first information matching module 4, the second information matching module 5, and the black voiceprint adding module 6 may be at least partially implemented as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented by hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or implemented by any one of three implementation manners of software, hardware, and firmware, or by a suitable combination of any of the three implementation manners.
Alternatively, at least one of the bank card application request module 1, the first voiceprint obtaining module 2, the second voiceprint obtaining module 3, the first information matching module 4, the second information matching module 5 and the black voiceprint adding module 6 may be at least partially implemented as a computer program module, and when the computer program module is executed, the corresponding function may be executed.
Fig. 12 schematically illustrates a block diagram of an electronic device suitable for implementing a voiceprint recognition based banking card application wind control method according to an embodiment of the present disclosure.
As shown in fig. 12, an electronic apparatus 900 according to an embodiment of the present disclosure includes a processor 901 which can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)902 or a program loaded from a storage portion 908 into a Random Access Memory (RAM) 903. Processor 901 may comprise, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. The processor 901 may also include on-board memory for caching purposes. The processor 901 may comprise a single processing unit or a plurality of processing units for performing the different actions of the method flows according to embodiments of the present disclosure.
In the RAM 903, various programs and data necessary for the operation of the electronic apparatus 900 are stored. The processor 901, the ROM 902, and the RAM 903 are connected to each other through a bus 904. The processor 901 performs various operations of the method flows according to the embodiments of the present disclosure by executing programs in the ROM 902 and/or the RAM 903. Note that the programs may also be stored in one or more memories other than the ROM 902 and the RAM 903. The processor 901 may also perform various operations of the method flows according to embodiments of the present disclosure by executing programs stored in the one or more memories.
Electronic device 900 may also include input/output (I/O) interface 905, input/output (I/O) interface 905 also connected to bus 904, according to an embodiment of the present disclosure. The electronic device 900 may also include one or more of the following components connected to the I/O interface 905: an input portion 906 including a keyboard, a mouse, and the like; an output section 907 including components such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 908 including a hard disk and the like; and a communication section 909 including a network interface card such as a LAN card, a modem, or the like. The communication section 909 performs communication processing via a network such as the internet. The driver 910 is also connected to an input/output (I/O) interface 905 as necessary. A removable medium 911 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 910 as necessary, so that a computer program read out therefrom is mounted into the storage section 908 as necessary.
The present disclosure also provides a computer-readable storage medium, which may be contained in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the disclosure.
According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, according to embodiments of the present disclosure, a computer-readable storage medium may include the ROM 902 and/or the RAM 903 described above and/or one or more memories other than the ROM 902 and the RAM 903.
Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the method illustrated in the flow chart. The program code is for causing a computer system to perform the methods of the embodiments of the present disclosure when the computer program product is run on the computer system.
The computer program performs the above-described functions defined in the system/apparatus of the embodiments of the present disclosure when executed by the processor 901. The systems, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
In one embodiment, the computer program may be hosted on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted, distributed in the form of a signal on a network medium, and downloaded and installed through the communication section 909 and/or installed from the removable medium 911. The computer program containing program code may be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 909, and/or installed from the removable medium 911. The computer program, when executed by the processor 901, performs the above-described functions defined in the system of the embodiment of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
In accordance with embodiments of the present disclosure, program code for executing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, these computer programs may be implemented using high level procedural and/or object oriented programming languages, and/or assembly/machine languages. The programming language includes, but is not limited to, programming languages such as Java, C + +, python, the "C" language, or the like. The program code may execute entirely on the user computing device, partly on the user device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Those skilled in the art will appreciate that various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.
The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.

Claims (15)

1. A bank card application wind control method based on voiceprint recognition is characterized by comprising the following steps:
receiving a bank card application request of a user;
when a user applies for a bank card, acquiring first voiceprint information of the user;
when a user is investigated, second voiceprint information of the user is obtained;
determining a first similarity of the first voiceprint information and the second voiceprint information;
when the first similarity is larger than a first threshold value, determining a second similarity between the second voiceprint information and the black voiceprint in the black voiceprint library; and
and when the second similarity is larger than a second threshold value, storing the first voiceprint information and/or the second voiceprint information to the black voiceprint library.
2. The method according to claim 1, wherein when the first similarity is smaller than the first threshold, the first voiceprint information and/or the second voiceprint information is stored to the black voiceprint library.
3. The method of claim 1, further comprising: after the first voiceprint information of the user is obtained, storing the first voiceprint information into a temporary voiceprint library, wherein the temporary voiceprint library comprises at least one temporary voiceprint;
the determining a first similarity of the first voiceprint information and the second voiceprint information comprises:
matching the second voiceprint information to the temporary voiceprint in the temporary voiceprint repository;
acquiring a similarity value list of the second voiceprint information and the temporary voiceprint; and
and acquiring the first similarity of the second voiceprint information and the first voiceprint information.
4. The method of claim 1, wherein determining the second similarity of the second voiceprint information to a black voiceprint in a black voiceprint library comprises:
acquiring a similarity value list of the second voiceprint information and the black voiceprint; and
and acquiring the second similarity of the second voiceprint information and the black voiceprint.
5. The method of claim 1, wherein the obtaining user first voiceprint information comprises:
displaying the text information;
recording voice prints of the text information read by the user; and
and denoising the voiceprint, wherein the voiceprint subjected to denoising is the first voiceprint information.
6. The method of claim 1, wherein the obtaining user first voiceprint information comprises:
displaying the text information;
recording voice prints of the text information read by the user;
denoising the voiceprint, wherein denoising the voiceprint comprises removing noise irrelevant to the reading of the text information in the voiceprint to obtain a first effective voiceprint; and
and extracting the voiceprint characteristics of the first effective voiceprint, wherein the voiceprint characteristics are the first voiceprint information.
7. The method of claim 6, wherein the obtaining the first voiceprint information of the user further comprises:
setting a first effective voiceprint progress bar, wherein the first effective voiceprint progress bar is the duration of the first effective voiceprint, recording noise reduction processing after the voiceprint, if the first effective voiceprint progress bar does not meet the threshold, continuing recording the voiceprint of the text information, the noise reduction processing after the voiceprint, until the first effective voiceprint progress bar meets the threshold, extracting the voiceprint characteristic of the first effective voiceprint is that the first effective voiceprint progress bar meets the voiceprint characteristic of the first effective voiceprint after the threshold.
8. The method of claim 6, wherein extracting the voiceprint features of the first valid voiceprint comprises extracting the voiceprint features using a neural network model.
9. The method of claim 8, wherein the neural network model is a gaussian mixture model-generic background model.
10. The method according to any one of claims 1-9, wherein the obtaining user second voiceprint information comprises:
the bank dials the contact information filled in when initiating the bank card application request; and
and acquiring audio information of a listener, wherein the audio information is the second voiceprint information.
11. The method according to any one of claims 1-9, wherein the obtaining user second voiceprint information comprises:
the bank dials the contact information filled in when initiating the bank card application request;
collecting audio information of a listener;
denoising the audio information, wherein denoising the audio information comprises removing noise irrelevant to conversation content in the audio information to obtain a second effective voiceprint; and
and extracting the voiceprint characteristics of the second effective voiceprint, wherein the voiceprint characteristics are the second voiceprint information.
12. The utility model provides a bank card application wind accuse device based on voiceprint discernment which characterized in that includes:
the bank card application request module is used for executing and receiving a bank card application request of a user;
the first voiceprint acquisition module is used for acquiring first voiceprint information of a user when the user applies for a bank card;
the second acoustic line acquisition module is used for acquiring second acoustic line information of the user when the user is investigated;
the first information matching module is used for determining the first similarity of the first voiceprint information and the second voiceprint information;
the second information matching module is used for determining a second similarity between the second voiceprint information and the black voiceprint in the black voiceprint library when the first similarity is larger than a first threshold; and
and the black voiceprint adding module is used for storing the first voiceprint information and/or the second voiceprint information to the black voiceprint library when the second similarity is larger than a second threshold.
13. An electronic device, comprising:
one or more processors;
one or more memories for storing executable instructions that, when executed by the processor, implement the method of any of claims 1-11.
14. A computer-readable storage medium having stored thereon executable instructions that when executed by a processor implement a method according to any one of claims 1 to 11.
15. A computer program product comprising one or more executable instructions which, when executed by a processor, implement a method according to any one of claims 1 to 11.
CN202110650576.2A 2021-06-10 2021-06-10 Bank card application wind control method and device, electronic equipment and medium Pending CN113393318A (en)

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