WO2019196303A1

WO2019196303A1 - User identity authentication method, server and storage medium

Info

Publication number: WO2019196303A1
Application number: PCT/CN2018/102123
Authority: WO
Inventors: 王健宗; 胡秋涵; 李梦迪; 郑斯奇; 肖京
Original assignee: 平安科技（深圳）有限公司
Priority date: 2018-04-09
Filing date: 2018-08-24
Publication date: 2019-10-17
Also published as: CN108766444A; CN108766444B

Abstract

Provided is a user identity authentication method, comprising: receiving an identity authentication request carrying an identity identifier of a target user, and acquiring current voice data of the target user from a client; inputting the current voice data into a trained voiceprint recognition model, determining a current voiceprint feature vector of the target user, and determining a standard voiceprint feature vector corresponding to the identity identifier of the target user; calculating the distance between the current voiceprint feature vector and the standard voiceprint feature vector; and analyzing, according to the distance, whether the target user passes the identity authentication, and sending an identity authentication result to the client. Further provided are an identity authentication server and a computer-readable storage medium. By using the present application, the problem of there being a greater difference between a voiceprint identification vector and an actual voiceprint identification vector caused by the difference between voice data collection channels can be avoided, thereby improving the accuracy of identity authentication.

Description

User authentication method, server and storage medium

The present application is based on the priority of the Chinese Patent Application entitled "User Authentication Method, Server and Storage Medium", filed on April 9, 2018, with the application number of CN 2018103110980, the entire contents of which are The manner of reference is incorporated in the present application.

Technical field

The present application relates to the field of computer technologies, and in particular, to a user identity verification method, a server, and a computer readable storage medium.

Background technique

At present, with the continuous development of voiceprint recognition technology, the use of voiceprint verification technology to verify user identity has become an important means of authentication for major customer service companies (eg, banks, insurance companies, game companies, etc.).

The traditional business solution for realizing user authentication using voiceprint verification technology is: the existing voiceprint recognition technology, which usually uses a voiceprint verification model from the data collected by a single channel, and then uses the trained voice. The pattern verification model performs voiceprint verification on voiceprint data from different channels.

However, the drawback of this conventional voiceprint verification scheme is that when used across devices, the difference in voiceprint data collected is likely to be large due to the difference between different types of devices, and the accuracy of recognition cannot meet the requirements.

Summary of the invention

The present application provides a user identity verification method, a server, and a computer readable storage medium, the main purpose of which is to avoid the problem that the difference between the voiceprint discrimination vector and the actual voiceprint discrimination vector caused by different voice data collection channels is different, and the identity verification is improved. The accuracy.

To achieve the above objective, the present application provides a user identity verification method, including:

Receiving an authentication request with a target user identity, and obtaining current voice data of the target user from the client;

The current voice data is input into the trained voiceprint recognition model, and the current voiceprint feature vector of the target user is determined, and the target user identity is determined according to a predetermined mapping relationship between the user identity and the standard voiceprint feature vector. Corresponding standard voiceprint feature vector;

Calculating a distance between the current voiceprint feature vector and the standard voiceprint feature vector using a predetermined distance calculation formula; and

And analyzing, according to the distance, whether the target user is authenticated, and sending the identity verification result to the client.

In addition, in order to achieve the above object, the present application further provides an identity verification server, where the server includes a memory and a processor, and the memory stores a user identity verification program executable on the processor, the program is processed by the processor. The following steps are implemented during execution:

In addition, in order to achieve the above object, the present application further provides a computer readable storage medium having a user identity verification program stored thereon, and the program is implemented by a processor to implement the user identity verification method as described above. Any step.

Compared with the prior art, the user identity verification method, the server and the computer readable storage medium proposed by the present application, by redefining the voiceprint recognition model, and using the voiceprint data collected by different channels, the voiceprint recognition model is obtained from the current The current voiceprint discrimination vector of the target user is extracted from the voice data, which avoids the problem that the voiceprint discrimination vector and the actual voiceprint discrimination vector are different due to different voice data collection channels, and improves the extracted voiceprint discrimination vector. Accuracy; calculating the distance between the current voiceprint discrimination vector and the standard voiceprint discrimination vector corresponding to the predetermined user identity, and determining whether the target user identity is included in the user identity corresponding to the preset minimum distance Analyze whether the target user authentication is passed, which improves the success rate of user authentication to a certain extent.

DRAWINGS

1 is a schematic diagram of a preferred embodiment of a user identity verification server of the present application;

2 is a schematic diagram of a program module of the user identity verification program in FIG. 1;

3 is a flow chart of a preferred embodiment of a user identity verification method of the present application.

The implementation, functional features and advantages of the present application will be further described with reference to the accompanying drawings.

detailed description

It is understood that the specific embodiments described herein are merely illustrative of the application and are not intended to be limiting.

The application provides a user identity verification server 1. Referring to FIG. 1, a schematic diagram of a preferred embodiment of the identity verification server 1 of the present application is shown.

In this embodiment, the authentication server 1 may be a rack server, a blade server, a tower server, or a rack server.

The authentication server 1 includes a memory 11, a processor 12, a communication bus 13, and a network interface 14.

The memory 11 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (for example, an SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, and the like. The memory 11 may in some embodiments be an internal storage unit of the authentication server 1, such as the hard disk of the authentication server 1. The memory 11 may also be an external storage device of the authentication server 1 in other embodiments, such as a plug-in hard disk equipped with the smart card (SMC), a secure digital card (SMC). Secure Digital, SD) cards, flash cards, etc. Further, the memory 11 may also include both an internal storage unit of the authentication server 1 and an external storage device. The memory 11 can be used not only for storing the application software installed in the identity verification server 1 and various types of data, such as the user identity verification program 10, the mapping relationship between the predetermined user identity and the standard voiceprint authentication vector, and the like. The data that has been output or will be output is temporarily stored.

The processor 12 can be a central processing unit (CPU), controller, microcontroller, microprocessor or other data processing chip for running program code or processing data stored in the memory 11, such as user identity. Verification procedure 10, etc.

Communication bus 13 is used to implement connection communication between these components.

The network interface 14 may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), and is generally used to establish a communication connection between the authentication server 1 and other electronic devices, for example, the authentication server 1 passes through the network. The interface 14 receives the identity verification request that carries the target identity sent by the user through the client (not identified in the figure), and feeds the authentication result to the client.

Figure 1 shows only the authentication server 1 with components 11-14, but it should be understood that not all illustrated components may be implemented, and more or fewer components may be implemented instead.

Optionally, the authentication server 1 may further include a user interface, and the user interface may include a display, an input unit such as a keyboard, and the optional user interface may further include a standard wired interface and a wireless interface.

Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch liquid crystal display, and an Organic Light-Emitting Diode (OLED) touch device. The display may also be referred to as a display screen or display unit for displaying information processed in the authentication server 1 and a user interface for displaying visualizations.

In the embodiment shown in FIG. 1, a user identity verification program 10 is stored in the memory 11. When the processor 12 executes the user identity verification program 10 stored in the memory 11, the following steps are implemented:

In this embodiment, the client is a client computer or a mobile terminal with voice collection function used by the target user, and the target user sends an identity verification request through the client. After receiving the authentication request with the target user identity (for example, the ID number) sent by the client, in order to prevent the target user from performing a fake operation, the real-time voice data of the user who currently sends the authentication request is collected. That is, the client collects the current voice data of the target user, and constructs a corresponding current voiceprint discrimination vector for the collected current voice data. In addition, a corresponding standard voiceprint authentication vector is set in advance for a predetermined user identity, and a mapping relationship between the predetermined user identity and the standard voiceprint authentication vector is obtained, and the mapping relationship is saved to a database (not identified in the figure). The predetermined user identity identifies the target user identity. For example, the user identity M ₁ corresponds to a standard voiceprint discrimination vector.

User identity M ₂ corresponds to standard voiceprint identification vector

Then, according to the target user identity carried in the identity verification request, the mapping relationship between the user identity and the standard voiceprint authentication vector is retrieved from the database, and the standard voiceprint authentication vector corresponding to the target user identity is determined.

As an implementation manner, after the current voice data of the target user is collected, the current voice data is input into the pre-trained voiceprint recognition model to determine a current voiceprint discrimination vector corresponding to the current voice data.

Specifically, the voiceprint recognition model is obtained by acquiring a first preset number (for example, 5000) of voice samples of a user, and each user's voice sample includes a second preset number (for example, 10 copies). Different voice segment samples, wherein different voice segment samples are respectively acquired through different channels (for example, different terminals), and the preset voiceprint recognition model is trained by using the acquired voice samples of each user. Generate a trained voiceprint recognition model. By using the voiceprint data collected by different channels to train the voiceprint recognition model, the voiceprint recognition model can be used to obtain the voiceprint discrimination vector of the voice data from different channels, which can avoid the sound caused by different voice data collection channels to some extent. The difference between the discriminant vector and the actual voiceprint discriminant vector is large, and the accuracy of recognizing the voiceprint discriminant vector is improved.

Further, the voiceprint recognition model needs to be defined before training the voiceprint recognition model. In this embodiment, the voiceprint recognition model includes a speaker space feature item representing the eigenvoice space matrix and a channel space feature item representing the eigenchannel space matrix. It should be noted that the speaker spatial feature item is only related to the speaker and has nothing to do with the specific content of the speaker. The speaker's inter-class difference is expressed. To facilitate the algorithm calculation, the feature item is summarized and summarized into a matrix form, which is expressed as The eigenvoice space matrix, which contains the content defined as the speaker feature item, contains the information unique to the corresponding speaker, and the feature items are different for each person; the channel space feature item represents the same speaker Different differences, that is, noise differences caused by different channels, in order to facilitate the calculation of the algorithm, the feature items are summarized into a matrix form, representing the spatial matrix of the intrinsic channel, and the content contained therein is defined as a channel space feature item, which includes The voiceprint difference information brought by the same speaker through different channels, that is, the same voice of the same person after the same voice passes through different channels, the feature item is different. The speaker spatial feature item includes a speaker voiceprint feature vector, and the channel spatial feature item includes a channel factor feature vector.

Preferably, the model formula of the voiceprint recognition model is:

X _ij =μ+Fh _i +Gw _ij +∈ _ij

Where X _ij represents the jth speech of the i-th speaker, μ represents the mean of all speech sample data, F represents the identity space and contains the base used to represent various identities, and each column of F is equivalent to the inter-class The feature vector of space, h _i represents the voiceprint feature vector of the i-th speaker, G represents the error space and contains the base used to represent the different changes of the same identity, and each column of G is equivalent to the feature vector of the intra-class space, w _ij denotes a channel vector of the j-th element wherein the i-th voice speaker, ∈ _ij represents the residual noise term to denote not yet explain the factors, that may have a Gaussian distribution of zero, "μ + Fh _i" represents the speaker The human space feature term, "Gw _ij + ∈ _ij ", represents a channel space feature item. It should be noted that the voiceprint feature vectors h _i corresponding to different speech segments of the same speaker are the same, and the Gw _ij +∈ _ij factor relationship can be trained through model training.

After extracting the current voiceprint discrimination vector corresponding to the current voice data of the target user by using the voiceprint recognition model, calculating a current voiceprint discrimination vector corresponding to the target voiceprint discrimination vector corresponding to the target user identity identifier according to the predetermined distance calculation formula. the distance. As an implementation manner, the predetermined distance calculation formula may be:

Where D represents the distance between the current voiceprint discrimination vector and the standard voiceprint discrimination vector corresponding to the target user identity.

Representing the standard voiceprint identification vector corresponding to the target user identity carried in the authentication request,

Represents the current voiceprint discrimination vector extracted from the current voice data.

It can be understood that the greater the distance between the current voiceprint discrimination vector and the standard voiceprint discrimination vector, the less likely the speaker corresponding to the two vectors is the same person. Therefore, a distance threshold is preset. When the calculated distance is less than or equal to the preset distance threshold, the voiceprint verification result is determined to be voiceprint verification, that is, the target user identity verification is determined; otherwise, the voiceprint verification result is voiceprint. The verification fails, that is, the target user authentication fails, and the authentication result is fed back to the client.

In other embodiments, after determining the current voiceprint discrimination vector corresponding to the current voice data of the target user and the standard voiceprint discrimination vector corresponding to the target user identity, the current voiceprint feature vector is calculated by using a predetermined distance calculation formula. While calculating the distance between the standard voiceprint feature vectors corresponding to the target user identity, the current voiceprint feature vector is also calculated corresponding to each of the predetermined (eg, n, n is an integer, and n>0) other users. Pre-storing a plurality of distances between standard voiceprint feature vectors, that is, respectively calculating a distance D _i between the current voiceprint discrimination vector and a standard voiceprint discrimination vector corresponding to all of the predetermined user identifiers, wherein i The value is an integer, and 0<i ≤ n. The specific calculation manner is the same as the above embodiment, and is not described here.

Further, in order from large to small, the distance between the current voiceprint feature vector and the standard voiceprint feature vector corresponding to each predetermined user identity is sorted, and each predetermined user identity is determined. The target user identity is included; the third preset number (for example, 5) of the user identity corresponding to the previous distance is filtered out from the n distances, and the third preset number (for example, 5) is determined. Whether the target user identity is included in the user identifier; when the third preset number (for example, 5) of the user identity includes the target user identity, the voiceprint verification result is determined to be voiceprint verification, that is, The target user authentication is passed. Otherwise, it is determined that the voiceprint verification result is that the voiceprint verification fails, that is, the target user identity verification fails, and the authentication result is fed back to the client. It should be noted that the larger the third preset number is, the more likely the voiceprint recognition is to pass. However, the accuracy of the recognition cannot be guaranteed. Therefore, in order to improve the accuracy of voiceprint verification, it may be based on actual needs. The sorted order is adjusted in the previous third preset number (for example, the third preset number is adjusted to 2).

The server 1 proposed by the above embodiment extracts the current voiceprint discrimination vector of the target user from the current voice data by redefining the voiceprint recognition model and using the voiceprint data trained by the voiceprint data collected by different channels. To the extent that the difference between the voiceprint discrimination vector and the actual voiceprint discrimination vector caused by different voice data collection channels is avoided, and the accuracy of extracting the voiceprint discrimination vector is improved; by calculating the current voiceprint discrimination vector and the predetermined user The distance between the standard voiceprint authentication vectors corresponding to the identity identifier, and whether the target user identity identifier is included in the user identity corresponding to the preset minimum number of distances, and whether the target user identity verification is passed, thereby improving the user to a certain extent The success rate of authentication.

Optionally, in other embodiments, the user identity verification program 10 may also be divided into one or more modules, one or more modules are stored in the memory 11 and executed by one or more processors (this implementation) For example, the processor 12) is executed to complete the application, and a module referred to herein refers to a series of computer program instructions that are capable of performing a particular function. For example, as shown in FIG. 2, it is a schematic diagram of a program module of the user identity verification program 10 in FIG. 1. In this embodiment, the user identity verification program 10 can be divided into an acquisition module 110, a vector extraction module 120, a calculation module 130, and an analysis. The functions or operational steps implemented by the modules 140-140 are similar to the above, and are not described in detail herein, by way of example, for example:

The obtaining module 110 is configured to receive an identity verification request with a target user identity, and obtain current voice data of the target user from the client.

The vector extraction module 120 is configured to input the current voice data into the trained voiceprint recognition model, determine a current voiceprint feature vector of the target user, and according to a mapping relationship between the predetermined user identity and the standard voiceprint feature vector, Determining a standard voiceprint feature vector corresponding to the target user identity;

The calculating module 130 is configured to calculate a distance between the current voiceprint feature vector and the standard voiceprint feature vector by using a predetermined distance calculation formula; and

The analyzing module 140 is configured to analyze, according to the distance, whether the target user passes the identity verification, and send the identity verification result to the client.

In addition, the present application also provides a user identity verification method. Referring to FIG. 3, it is a flowchart of a preferred embodiment of the user identity verification method of the present application. The method can be performed by a device that can be implemented by software and/or hardware.

In this embodiment, the user identity verification method includes steps S1-S4:

Step S1: Receive an identity verification request with a target user identity, and obtain current voice data of the target user from the client.

Step S2: input the current voice data into the trained voiceprint recognition model, determine a current voiceprint feature vector of the target user, and determine the target according to a predetermined mapping relationship between the user identity identifier and the standard voiceprint feature vector. a standard voiceprint feature vector corresponding to the user identity;

Step S3, calculating a distance between the current voiceprint feature vector and the standard voiceprint feature vector by using a predetermined distance calculation formula; and

Step S4: Analyze, according to the distance, whether the target user passes the identity verification, and sends the identity verification result to the client.

User identity M ₂ corresponds to standard voiceprint identification vector

Preferably, the model formula of the voiceprint recognition model is:

X _ij =μ+Fh _i +Gw _ij +∈ _ij

Where X _ij represents the jth speech of the i-th speaker, μ represents the mean of all speech sample data, F represents the identity space and contains the base used to represent various identities, and each column of F is equivalent to the inter-class The feature vector of space, h _i represents the voiceprint feature vector of the i-th speaker, G represents the error space and contains the base used to represent the different changes of the same identity, and each column of G is equivalent to the feature vector of the intra-class space, w _ij represents the channel eigenvectors j-th element of the i-th voice speaker, ∈ _ij represents the residual noise term to denote not yet explain the factors, that may have a Gaussian distribution of zero, "μ + Fh _i" represents the speaker The human space feature term, "Gw _ij + ∈ _ij ", represents a channel space feature item. It should be noted that the voiceprint feature vectors h _i corresponding to different speech segments of the same speaker are the same, and the Gw _ij +∈ _ij factor relationship can be trained through model training.

In other embodiments, the step S3 may be replaced by calculating a distance between the current voiceprint feature vector and the standard voiceprint feature vector corresponding to each of the predetermined user identity identifiers by using a predetermined distance calculation formula.

After determining the current voiceprint discrimination vector corresponding to the current voice data of the target user and the standard voiceprint discrimination vector corresponding to the target user identity, respectively, calculating the current voiceprint feature vector and the target user identity identifier by using a predetermined distance calculation formula While calculating the distance between the standard voiceprint feature vectors, the current voiceprint feature vector is also calculated with each predetermined (eg, n, n is an integer, and n>0) pre-stored standard voiceprint feature vectors corresponding to other users. a plurality of distances between each other, that is, respectively calculating a distance D _i between the current voiceprint discrimination vector and a standard voiceprint discrimination vector corresponding to all of the predetermined user identifiers described above, where i is an integer and 0< i ≤ n, the specific calculation method is consistent with the above embodiment, and will not be described herein.

Further, the step S4 may be replaced by: sorting the distance between the current voiceprint feature vector and the standard voiceprint feature vector corresponding to each predetermined user identity according to the order from large to small, Determining the user identity of the user in the predetermined user identity; selecting a third preset number (for example, 5) of the user identity corresponding to the previous distance from the n distances, and determining the third pre-determination Whether the number of user IDs (for example, 5) includes the target user identity; when the third preset number (for example, 5) of the user identity includes the target user identity, the voiceprint verification is determined. The result is that the voiceprint verification is passed, that is, the target user identity is passed. Otherwise, the voiceprint verification result is determined to be that the voiceprint verification fails, that is, the target user identity verification fails, and the identity verification result is fed back to the client. It should be noted that the larger the third preset number is, the more likely the voiceprint recognition is to pass. However, the accuracy of the recognition cannot be guaranteed. Therefore, in order to improve the accuracy of voiceprint verification, it may be based on actual needs. The sorted order is adjusted in the previous third preset number (for example, the third preset number is adjusted to 2).

The user identity verification method proposed by the above embodiment extracts the current voiceprint discrimination vector of the target user from the current voice data by redefining the voiceprint recognition model and using the voiceprint recognition model trained by the voiceprint data collected by different channels. To some extent, the problem that the voiceprint discrimination vector and the actual voiceprint discrimination vector are different due to different voice data collection channels is avoided, and the accuracy of extracting the voiceprint discrimination vector is improved; by calculating the current voiceprint discrimination vector and predetermining The distance between the standard voiceprint authentication vectors corresponding to the user identity, and whether the target user identity is included in the user identity corresponding to the preset minimum number of distances, and whether the target user identity verification is passed, to some extent The success rate of user authentication.

In addition, the embodiment of the present application further provides a computer readable storage medium, where the user identifiable program 10 is stored, and when the program is executed by the processor, the following operations are implemented:

The specific embodiment of the computer readable storage medium of the present application is substantially the same as the embodiments of the user identity verification method described above, and is not described herein.

It should be noted that the foregoing serial numbers of the embodiments of the present application are merely for the description, and do not represent the advantages and disadvantages of the embodiments. And the terms "including", "comprising", or any other variations thereof are intended to encompass a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a plurality of elements includes not only those elements but also Other elements listed, or elements that are inherent to such a process, device, item, or method. An element that is defined by the phrase "comprising a ..." does not exclude the presence of additional equivalent elements in the process, the device, the item, or the method that comprises the element.

Through the description of the above embodiments, those skilled in the art can clearly understand that the foregoing embodiment method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be through hardware, but in many cases, the former is better. Implementation. Based on such understanding, the technical solution of the present application, which is essential or contributes to the prior art, may be embodied in the form of a software product stored in a storage medium (such as ROM/RAM as described above). , a disk, an optical disk, including a number of instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the methods described in the various embodiments of the present application.

The above is only a preferred embodiment of the present application, and is not intended to limit the scope of the patent application, and the equivalent structure or equivalent process transformations made by the specification and the drawings of the present application, or directly or indirectly applied to other related technical fields. The same is included in the scope of patent protection of this application.

Claims

A user identity verification method, the method comprising:

Receiving an authentication request with a target user identity, and obtaining current voice data of the target user from the client;

The current voice data is input into the trained voiceprint recognition model, and the current voiceprint feature vector of the target user is determined, and the target user identity is determined according to a predetermined mapping relationship between the user identity and the standard voiceprint feature vector. Corresponding standard voiceprint feature vector;

Calculating a distance between the current voiceprint feature vector and the standard voiceprint feature vector using a predetermined distance calculation formula; and

And analyzing, according to the distance, whether the target user is authenticated, and sending the identity verification result to the client.
The user identity verification method according to claim 1, wherein the step of "analysing whether the target user is authenticated according to the distance" comprises:

When the calculated distance is less than or equal to the preset threshold, it is determined that the target user identity verification is passed; or

When the calculated distance is greater than the preset threshold, it is determined that the target user identity verification fails.
The user identity verification method according to claim 1 or 2, wherein the training process of the voiceprint recognition model comprises:

Pre-acquiring a first preset number of voice samples of the user, each voice sample of the user includes a second preset number of different voice segment samples, and training the preset type by using the acquired voice samples of each of the users The voiceprint recognition model generates a trained voiceprint recognition model.
The user identity verification method according to claim 3, wherein the voiceprint recognition model comprises: a user space feature item representing an eigenvoice space matrix and a channel space feature item representing an eigenchannel space matrix, The user space feature item includes a user voiceprint feature vector, and the channel space feature item includes a channel factor feature vector.
The user identity verification method according to claim 4, wherein the formula of the voiceprint recognition model is:

X ij =μ+Fh i +Gw ij +∈ ij

Where X ij represents the jth speech of the i-th speaker, μ represents the mean of all speech sample data, F represents the identity space and contains the base used to represent various identities, and each column of F is equivalent to the inter-class The feature vector of space, h i represents the voiceprint feature vector of the i-th speaker, G represents the error space and contains the base used to represent the different changes of the same identity, and each column of G is equivalent to the feature vector of the intra-class space, w ij denotes a channel factor eigenvectors j-th voice of the i-th speaker, ∈ ij represents a residual noise term, "μ + Fh i" denotes the speaker space wherein items, "Gw ij + ∈ ij" represents a channel space wherein items .
The user identity verification method according to claim 1, wherein the step of "calculating the distance between the current voiceprint feature vector and the standard voiceprint feature vector by using a predetermined distance calculation formula" may Replace with:

The distance between the current voiceprint feature vector and the standard voiceprint feature vector corresponding to each predetermined user identity is calculated using a predetermined distance calculation formula.
The user identity verification method according to claim 6, wherein the step of "analysing whether the target user is authenticated according to the distance" comprises:

Sorting the distance between the current voiceprint feature vector and the standard voiceprint feature vector corresponding to each predetermined user identity in an order from large to small, wherein each predetermined user identity includes a target User identity

And filtering a third preset number of user identifiers corresponding to the previous distance, and determining whether the third preset number of user identifiers include the target user identifier;

When the third preset number of user identifiers include the target user identity, determining that the target user identity is verified; or

When the target user identity is not included in the third preset number of user identifiers, it is determined that the target user identity verification fails.
A user authentication server, characterized in that the server comprises: a memory, a processor, and a user identity verification program runable on the processor, the program being implemented by the processor The following steps:

Receiving an authentication request with a target user identity, and obtaining current voice data of the target user from the client;

The current voice data is input into the trained voiceprint recognition model, and the current voiceprint feature vector of the target user is determined, and the target user identity is determined according to a predetermined mapping relationship between the user identity and the standard voiceprint feature vector. Corresponding standard voiceprint feature vector;

Calculating a distance between the current voiceprint feature vector and the standard voiceprint feature vector using a predetermined distance calculation formula; and

And analyzing, according to the distance, whether the target user is authenticated, and sending the identity verification result to the client.
The user identity verification server according to claim 8, wherein the step of "analysing whether the target user is authenticated according to the distance" comprises:

When the calculated distance is less than or equal to the preset threshold, it is determined that the target user identity verification is passed; or

When the calculated distance is greater than the preset threshold, it is determined that the target user identity verification fails.
The user identity verification server according to claim 8 or 9, wherein the training process of the voiceprint recognition model comprises:

Pre-acquiring a first preset number of voice samples of the user, each voice sample of the user includes a second preset number of different voice segment samples, and training the preset type by using the acquired voice samples of each of the users The voiceprint recognition model generates a trained voiceprint recognition model.
The user identity verification server according to claim 10, wherein said voiceprint recognition model comprises: a user space feature item representing an eigenvoice space matrix and a channel space feature item representing an eigenchannel space matrix, The user space feature item includes a user voiceprint feature vector, and the channel space feature item includes a channel factor feature vector.
The user identity verification server according to claim 11, wherein the formula of the voiceprint recognition model is:

X ij =μ+Fh i +Gw ij +∈ ij

Where X ij represents the jth speech of the i-th speaker, μ represents the mean of all speech sample data, F represents the identity space and contains the base used to represent various identities, and each column of F is equivalent to the inter-class The feature vector of space, h i represents the voiceprint feature vector of the i-th speaker, G represents the error space and contains the base used to represent the different changes of the same identity, and each column of G is equivalent to the feature vector of the intra-class space, w ij denotes a channel factor eigenvectors j-th voice of the i-th speaker, ∈ ij represents a residual noise term, "μ + Fh i" denotes the speaker space wherein items, "Gw ij + ∈ ij" represents a channel space wherein items .
The user identity verification server according to claim 8, wherein said step of "calculating a distance between said current voiceprint feature vector and said standard voiceprint feature vector using a predetermined distance calculation formula" may Replace with:

The distance between the current voiceprint feature vector and the standard voiceprint feature vector corresponding to each predetermined user identity is calculated using a predetermined distance calculation formula.
The user identity verification server according to claim 13, wherein the step of "analysing whether the target user is authenticated according to the distance" comprises:

Sorting the distance between the current voiceprint feature vector and the standard voiceprint feature vector corresponding to each predetermined user identity in an order from large to small, wherein each predetermined user identity includes a target User identity

And filtering a third preset number of user identifiers corresponding to the previous distance, and determining whether the third preset number of user identifiers include the target user identifier;

When the third preset number of user identifiers include the target user identity, determining that the target user identity is verified; or

When the target user identity is not included in the third preset number of user identifiers, it is determined that the target user identity verification fails.
A computer readable storage medium, wherein the computer readable storage medium stores a user identity verification program, and when the program is executed by the processor, the following steps are implemented:

Receiving an authentication request with a target user identity, and obtaining current voice data of the target user from the client;

The current voice data is input into the trained voiceprint recognition model, and the current voiceprint feature vector of the target user is determined, and the target user identity is determined according to a predetermined mapping relationship between the user identity and the standard voiceprint feature vector. Corresponding standard voiceprint feature vector;

Calculating a distance between the current voiceprint feature vector and the standard voiceprint feature vector using a predetermined distance calculation formula; and

And analyzing, according to the distance, whether the target user is authenticated, and sending the identity verification result to the client.
The computer readable storage medium of claim 15, wherein the step of "analysing whether the target user is authenticated according to the distance" comprises:

When the calculated distance is less than or equal to the preset threshold, it is determined that the target user identity verification is passed; or

When the calculated distance is greater than the preset threshold, it is determined that the target user identity verification fails.
The computer readable storage medium according to claim 15 or 16, wherein the training process of the voiceprint recognition model comprises:

Pre-acquiring a first preset number of voice samples of the user, each voice sample of the user includes a second preset number of different voice segment samples, and training the preset type by using the acquired voice samples of each of the users The voiceprint recognition model generates a trained voiceprint recognition model.
The computer readable storage medium of claim 17, wherein the voiceprint recognition model comprises: a user space feature item representing an eigenvoice space matrix and a channel space feature item representing an eigenchannel space matrix, The user space feature item includes a user voiceprint feature vector, and the channel space feature item includes a channel factor feature vector.
The computer readable storage medium according to claim 15, wherein said step of "calculating a distance between said current voiceprint feature vector and said standard voiceprint feature vector using a predetermined distance calculation formula" Can be replaced by:

The distance between the current voiceprint feature vector and the standard voiceprint feature vector corresponding to each predetermined user identity is calculated using a predetermined distance calculation formula.
The computer readable storage medium of claim 19, wherein the step of "analysing whether the target user is authenticated according to the distance" comprises:

Sorting the distance between the current voiceprint feature vector and the standard voiceprint feature vector corresponding to each predetermined user identity in an order from large to small, wherein each predetermined user identity includes a target User identity

And filtering a third preset number of user identifiers corresponding to the previous distance, and determining whether the third preset number of user identifiers include the target user identifier;

When the third preset number of user identifiers include the target user identity, determining that the target user identity is verified; or

When the target user identity is not included in the third preset number of user identifiers, it is determined that the target user identity verification fails.