CN111339517A - Voiceprint feature sampling method, user identification method, device and electronic equipment - Google Patents
Voiceprint feature sampling method, user identification method, device and electronic equipment Download PDFInfo
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- CN111339517A CN111339517A CN202010413953.6A CN202010413953A CN111339517A CN 111339517 A CN111339517 A CN 111339517A CN 202010413953 A CN202010413953 A CN 202010413953A CN 111339517 A CN111339517 A CN 111339517A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
- G06F21/31—User authentication
- G06F21/32—User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L17/00—Speaker identification or verification
- G10L17/22—Interactive procedures; Man-machine interfaces
Abstract
The embodiment of the specification provides a voiceprint feature sampling method, a user identification method, a device and electronic equipment. The sampling method comprises the following steps: and when the target user performs man-machine interaction, acquiring the voiceprint characteristics and the non-voiceprint biological characteristics of the target user. And carrying out identity verification on the target user based on the collected non-voiceprint biological characteristics. And after the identity authentication is successful, storing the acquired voiceprint characteristics as reference voiceprint characteristics of the target user into a characteristic library. The user identification method comprises the following steps: and acquiring voice data provided by a user to be identified. And extracting the voiceprint characteristics of the user to be recognized from the voice data of the user to be recognized. And acquiring the reference voiceprint characteristics of the target user reserved in the characteristic library. And performing approximate matching on the voiceprint features of the user to be recognized and the reference voiceprint features of the target user, and determining whether the object to be recognized is the target user.
Description
Technical Field
The present disclosure relates to the field of biometric identification technologies, and in particular, to a method and an apparatus for sampling voiceprint features, and an electronic device.
Background
With the rapid development of the internet of things, big data, cloud computing and artificial intelligence, the biological identification technology is widely applied. In the large context of higher requirements on security, biometric identification is gradually evolved from today's singulated fingerprint identification or face identification to multimodal identification.
Currently, speech recognition is one of the more popular voice-based recognition methods. The principle of voice recognition is to recognize what the user says, so that the user needs to read fixed content in a matching way to make sound sample reservation, obviously, the experience brought to the user is poor, the user motility is low, and the popularity is limited. Therefore, there is a need for a more user-friendly voice recognition method that can sample voice with better user experience.
Disclosure of Invention
An embodiment of the present specification aims to provide a voiceprint feature sampling method, a user identification method, a device and an electronic device, which can perform voice sampling with better user experience.
In order to achieve the above object, the embodiments of the present specification are implemented as follows:
in a first aspect, a method for sampling a voiceprint feature is provided, including:
when a target user carries out human-computer interaction, acquiring voiceprint features and non-voiceprint biological features of the target user;
performing identity verification on the target user based on the collected non-voiceprint biological characteristics;
and after the identity authentication is successful, storing the acquired voiceprint characteristics as the reference voiceprint characteristics of the target user into a characteristic library.
In a second aspect, a user identification method based on voiceprint features is provided, including:
acquiring voice data provided by a user to be identified;
extracting voiceprint characteristics of the user to be recognized from the voice data of the user to be recognized;
acquiring reference voiceprint characteristics of a target user who reserves a sample in a characteristic library, wherein the reference voiceprint characteristics of the sample reserved in the characteristic library are acquired when the target user performs man-machine interaction, and based on non-voiceprint biological characteristics, except the voiceprint characteristics, acquired during the man-machine interaction, of the target user, after the target user is successfully authenticated, the reference voiceprint characteristics are stored in the characteristic library;
and performing approximate matching on the voiceprint features of the user to be recognized and the reference voiceprint features of the target user, and determining whether the object to be recognized is the target user.
In a third aspect, an apparatus for sampling voiceprint features, comprising:
the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring voiceprint characteristics and non-voiceprint biological characteristics except the voiceprint characteristics of a target user when the target user performs man-machine interaction;
the verification module is used for verifying the identity of the target user based on the collected non-voiceprint biological characteristics;
and the sample reserving module is used for storing the acquired voiceprint characteristics serving as the reference voiceprint characteristics of the target user into a characteristic library after the identity authentication is successful.
In a fourth aspect, an electronic device is provided comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program being executed by the processor to:
when a target user carries out human-computer interaction, acquiring voiceprint characteristics and non-voiceprint biological characteristics except the voiceprint characteristics of the target user;
performing identity verification on the target user based on the collected non-voiceprint biological characteristics;
and after the identity authentication is successful, storing the acquired voiceprint characteristics as the reference voiceprint characteristics of the target user into a characteristic library.
In a fifth aspect, a computer-readable storage medium is provided, having stored thereon a computer program which, when executed by a processor, performs the steps of:
when a target user carries out human-computer interaction, acquiring voiceprint characteristics and non-voiceprint biological characteristics except the voiceprint characteristics of the target user;
performing identity verification on the target user based on the collected non-voiceprint biological characteristics;
and after the identity authentication is successful, storing the acquired voiceprint characteristics as the reference voiceprint characteristics of the target user into a characteristic library.
In a sixth aspect, a user identification device based on voiceprint features is provided, which includes:
the acquisition module acquires voice data provided by a user to be identified;
the extraction module is used for extracting the voiceprint characteristics of the user to be identified from the voice data of the user to be identified;
the sampling module is used for acquiring the reference voiceprint characteristics of a target user who is reserved in a characteristic library, wherein the reference voiceprint characteristics of the reserved sample in the characteristic library are acquired when the target user performs man-machine interaction, and the target user is successfully authenticated and stored in the characteristic library based on the acquired non-voiceprint biological characteristics except the voiceprint characteristics during the man-machine interaction;
and the matching module is used for carrying out approximate matching on the voiceprint characteristics of the user to be recognized and the reference voiceprint characteristics of the target user and determining whether the object to be recognized is the target user.
In a seventh aspect, an electronic device is provided that includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program being executed by the processor to:
acquiring voice data provided by a user to be identified;
extracting voiceprint characteristics of the user to be recognized from the voice data of the user to be recognized;
acquiring reference voiceprint characteristics of a target user who reserves a sample in a characteristic library, wherein the reference voiceprint characteristics of the sample reserved in the characteristic library are acquired when the target user performs man-machine interaction, and based on non-voiceprint biological characteristics, except the voiceprint characteristics, acquired during the man-machine interaction, of the target user, after the target user is successfully authenticated, the reference voiceprint characteristics are stored in the characteristic library;
and performing approximate matching on the voiceprint features of the user to be recognized and the reference voiceprint features of the target user, and determining whether the object to be recognized is the target user.
In an eighth aspect, a computer readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring voice data provided by a user to be identified;
extracting voiceprint characteristics of the user to be recognized from the voice data of the user to be recognized;
acquiring reference voiceprint characteristics of a target user who reserves a sample in a characteristic library, wherein the reference voiceprint characteristics of the sample reserved in the characteristic library are acquired when the target user performs man-machine interaction, and based on non-voiceprint biological characteristics, except the voiceprint characteristics, acquired during the man-machine interaction, of the target user, after the target user is successfully authenticated, the reference voiceprint characteristics are stored in the characteristic library;
and performing approximate matching on the voiceprint features of the user to be recognized and the reference voiceprint features of the target user, and determining whether the object to be recognized is the target user.
The scheme of the embodiment of the specification can collect the voiceprint characteristics and the non-voiceprint biological characteristics of the user at any time and any place, carry out identity verification through the non-voiceprint biological characteristics, and carry out sample reservation on the voiceprint characteristics for user identification after the identity verification is successful. Obviously, the whole process can be carried out without the perception of the user, and the user does not need to specially cooperate with reading fixed content, so that the method has better user experience and plays a role in popularizing the multi-mode recognition. Meanwhile, "voiceprint recognition" is different from "voice recognition", and is to recognize "who is saying" rather than "what is saying", so the recognition accuracy is improved, and more reliable recognition and verification of safety is realized.
Drawings
In order to more clearly illustrate the embodiments of the present specification or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the embodiments of the present specification, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative efforts.
Fig. 1 is a schematic flow chart of a method for sampling a voiceprint feature provided in an embodiment of the present disclosure.
Fig. 2 is a schematic flowchart of a user identification method based on voiceprint features according to an embodiment of the present disclosure.
Fig. 3 is a schematic structural diagram of a sampling apparatus for voiceprint features provided in an embodiment of the present disclosure.
Fig. 4 is a schematic structural diagram of a user identification apparatus based on voiceprint features according to an embodiment of the present disclosure.
Fig. 5 is a schematic structural diagram of an electronic device provided in an embodiment of this specification.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only a part of the embodiments of the present specification, and not all of the embodiments. All other embodiments obtained by a person skilled in the art based on the embodiments in the present specification without any inventive step should fall within the scope of protection of the present specification.
As described above, in the context of higher requirements for security, biometric recognition is gradually evolving from today's singulated fingerprint recognition or face recognition into multi-modal recognition. Among them, voice recognition is an ideal non-fingerprint recognition method. Currently, speech recognition is the mainstream speech recognition method. The principle of speech recognition is to recognize what the user says, so that the user needs to cooperate with reading fixed content to make sound sample reservation, obviously, the experience brought to the user is poor, and the popularization is limited. For this reason, this document aims to propose a more user-friendly way of voice recognition, enabling voice sampling without the user's intention.
Fig. 1 is a flowchart of a sampling method of voiceprint features in an embodiment of the present description. The method shown in fig. 1 may be performed by a corresponding apparatus, comprising:
and S102, when the target user performs man-machine interaction, acquiring the voiceprint features and the non-voiceprint biological features of the target user.
Specifically, in this step, when the target user starts the voice interaction function of the terminal device, the voiceprint feature and the non-voiceprint biometric feature of the target user can be collected through the terminal device. Here, the voice interaction function of the terminal device opened by the user is not necessarily used for voice sample reservation, for example, when the user opens the interaction software to perform voice chat, or when the user opens the search engine software to perform voice search, the voiceprint feature of the target user is collected in an unconscious state of the target user.
And step S104, performing identity verification on the target user based on the collected non-voiceprint biological characteristics.
It is to be appreciated that embodiments of the present description may be adapted for use in a multimodal identification scheme for authenticating a target user through non-voiceprint biometrics. For example, facial features of a user are collected, and authentication is performed based on the facial features of the user.
And step S106, after the identity authentication is successful, storing the acquired voiceprint characteristics as reference voiceprint characteristics of the target user into a characteristic library.
It should be understood that the target user may be subsequently identified based on the voiceprint features of the corresponding records in the feature library.
The sampling method provided by the embodiment of the specification can be used for collecting the voiceprint characteristics and the non-voiceprint biological characteristics of the user at any time and any place, carrying out identity verification through the non-voiceprint biological characteristics, and after the identity verification is successful, reserving a sample of the voiceprint characteristics and then continuously using the sample for biological identification. Obviously, the whole process can be carried out without the perception of the user, and the user does not need to specially cooperate with reading fixed content, so that the method has better user experience and plays a role in popularizing the multi-mode recognition.
The method of the embodiments of the present disclosure is described below with reference to practical application scenarios.
The application scene further introduces biological recognition based on voiceprint characteristics on the basis of face recognition, so that multi-modal identity verification is realized. Correspondingly, the process mainly comprises the following steps:
step A1, when a target user and a terminal device perform man-machine interaction, starting a microphone and a camera of the terminal device, and collecting voiceprint features and face features (non-voiceprint biological features) of the target user;
as mentioned above, this step may utilize a common APP with a voice function to obtain the voiceprint feature of the target user. For example, when a target user uses an instant messaging APP to perform speech, speech data input by the target user may be acquired, and voiceprint features of the target user may be extracted from the speech data. Meanwhile, the terminal equipment can be controlled to start the camera, the face image of the target user is shot, and the face features of the target user are extracted from the face image. Obviously, the whole process of the step can be carried out in the background of the terminal device, and the influence on the normal use of the terminal device by the target user is avoided.
And step A2, performing identity verification on the target user based on the collected human face features. If the verification is successful, step A3 is executed, otherwise, the process is ended.
It should be understood that, in the application scenario, the target user enters the own reference facial features into the terminal device in advance, so that the facial features extracted in the step one can be compared with the reference facial features which are entered in advance to determine the identity of the target user.
Step a3, it is determined whether the feature library stores other reference voiceprint features of the target user in advance. If yes, go to step a4, if no, go to step a5 or step a6.
Step A4, approximately matching the collected voiceprint features with other reference voiceprint features of the target user pre-stored in the feature library. If the approximate match is successful, step A5 is performed. Otherwise, step a6 is performed.
And step A5, storing the collected voiceprint characteristics as the reference voiceprint characteristics of the target user into a characteristic library, and ending the process.
Step a6, initiating a preset deep authentication to the target user. If the deep identity authentication is successful, the process is ended after the step a5 is executed, otherwise, the process is ended directly.
The deep identity authentication comprises at least one of password authentication, answer authentication and verification code authentication.
Based on the application scenario, it can be known that the method in the embodiment of the present specification can adopt the face feature and the voice print feature in real time in the process of human-computer voice interaction by a target user, and the user can perform voiceprint copying many times in an uncoordinated and natural manner, so that the bad experience of performing voiceprint copying by the user in a hard reading manner is overcome, and the voiceprint feature can realize multi-modal user identification together with other identification modes including the face feature, so as to improve the identification performance.
Correspondingly, the embodiment of the specification further provides a user identification method based on the voiceprint features. Fig. 2 is a flowchart of a user identification method according to an embodiment of the present disclosure. The method shown in fig. 2 may be performed by a corresponding apparatus, comprising:
step S202, voice data provided by the user to be identified is acquired.
Step S204, extracting the voiceprint characteristics of the user to be recognized from the voice data of the user to be recognized.
And S206, acquiring the reference voiceprint characteristics of the target user who is reserved in the characteristic library, wherein the reference voiceprint characteristics of the reserved samples in the characteristic library are acquired when the target user performs man-machine interaction, and storing the target user into the characteristic library after the target user successfully performs identity verification based on the acquired non-voiceprint biological characteristics except the voiceprint characteristics during the man-machine interaction.
And S208, approximately matching the voiceprint characteristics of the user to be recognized with the reference voiceprint characteristics of the target user, and determining whether the object to be recognized is the target user.
It should be understood that the approximate matching method is not exclusive, and thus the embodiments of the present specification are not particularly limited. By way of exemplary presentation:
in this step, a data distance (e.g., a hamming distance, an euclidean distance, etc.) between the voiceprint feature of the user to be identified and the reference voiceprint feature of the target user can be calculated, and when the data example does not reach a preset standard value, it indicates that the user to be identified is the target user. Otherwise, the user to be identified is not the target user.
Or, the deep learning model may be trained by using the reference voiceprint feature of the target user in advance, the voiceprint feature of the user to be recognized is input into the deep learning model after training, and the deep learning model gives the recognition result of whether the user to be recognized is the target user.
The user identification method provided by the embodiment of the specification can be used for collecting the voiceprint characteristics and the non-voiceprint biological characteristics of the user at any time and any place, carrying out identity verification through the non-voiceprint biological characteristics, and reserving a sample for the voiceprint characteristics for user identification after the identity verification is successful. Obviously, the whole process can be carried out without the perception of the user, and the user does not need to specially cooperate with reading fixed content, so that better user experience is achieved. Meanwhile, "voiceprint recognition" is different from "voice recognition", and is to recognize "who is saying" rather than "what is saying", so the recognition accuracy is improved, and more reliable recognition and verification of safety is realized.
The above is an introduction of the method for sampling the voiceprint features and the subsequent method for identifying the user based on the voiceprint features in the embodiment of the present specification. It will be appreciated that appropriate modifications may be made without departing from the principles outlined herein, and such modifications are intended to be included within the scope of the embodiments herein.
Corresponding to the sampling method, the embodiment of the present specification further provides a sampling device for voiceprint features. Fig. 3 is a structural diagram of a sampling apparatus 300 according to an embodiment of the present disclosure, including:
the acquisition module 310 acquires a voiceprint feature and a non-voiceprint biometric feature except the voiceprint feature of a target user when the target user performs human-computer interaction.
And the verification module 320 is used for verifying the identity of the target user based on the collected non-voiceprint biological characteristics.
And the sample reserving module 330 is configured to store the acquired voiceprint features as reference voiceprint features of the target user in a feature library after the identity authentication is successful.
The sampling device of this description embodiment can gather user's vocal print characteristic and non-vocal print biological characteristics anytime and anywhere to carry out authentication through non-vocal print biological characteristics, after authentication succeeds, carry out the sample to vocal print characteristic and follow-up in order to be used for biological identification. Obviously, the whole process can be carried out without the perception of the user, and the user does not need to specially cooperate with reading fixed content, so that the method has better user experience and plays a role in popularizing the multi-mode recognition.
Optionally, when the sample keeping module 330 is executed, it is specifically determined whether a feature library stores other reference voiceprint features of the target user in advance; if so, performing approximate matching on the acquired voiceprint features and other reference voiceprint features of the target user, which are stored in the feature library in advance; and after the approximate matching is successful, storing the acquired voiceprint characteristics as the reference voiceprint characteristics of the target user into a characteristic library. And if not, directly storing the voiceprint feature of the target user as the reference voiceprint feature of the target user into a feature library.
Optionally, when the sample reserving module 330 is executed, if the approximate matching fails or the feature library does not store other reference voiceprint features of the target user in advance, a preset deep identity authentication is initiated to the target user, where the deep identity authentication includes at least one of password authentication, answer authentication, and verification code authentication; and after the deep identity authentication is successful, storing the acquired voiceprint characteristics as the reference voiceprint characteristics of the target user into a characteristic library.
Optionally, the non-voiceprint biometric characteristic comprises at least one of a fingerprint characteristic, a palm print characteristic, an iris characteristic, a lip language characteristic, and a face characteristic.
Obviously, the sampling apparatus of the embodiment of the present specification can be used as the execution subject of the sampling method shown in fig. 1, and thus can realize the functions of the sampling method realized in fig. 1. Since the principle is the same, the detailed description is omitted here.
Corresponding to the user identification method, the embodiment of the specification further provides a user identification device based on the voiceprint characteristics. Fig. 4 is a structural diagram of a user identification device according to an embodiment of the present specification, including:
the obtaining module 410 obtains voice data provided by a user to be recognized.
And the extracting module 420 is configured to extract the voiceprint feature of the user to be recognized from the voice data of the user to be recognized.
The sampling module 430 is configured to obtain a reference voiceprint feature of a target user who has a sample left in a feature library, where the reference voiceprint feature of the sample left in the feature library is acquired when the target user performs human-computer interaction, and based on a non-voiceprint biological feature other than the voiceprint feature acquired during the human-computer interaction, the reference voiceprint feature of the sample left in the feature library is stored in the feature library after the target user successfully performs identity verification.
The matching module 440 performs approximate matching on the voiceprint feature of the user to be recognized and the reference voiceprint feature of the target user, and determines whether the object to be recognized is the target user.
The user identification device of this specification embodiment can gather user's vocal print characteristic and non-vocal print biological characteristics anytime and anywhere to carry out authentication through non-vocal print biological characteristics, after authentication succeeds, carry out the reservation to the vocal print characteristic and be used for user identification. Obviously, the whole process can be carried out without the perception of the user, and the user does not need to specially cooperate with reading fixed content, so that better user experience is achieved. Meanwhile, "voiceprint recognition" is different from "voice recognition", and is to recognize "who is saying" rather than "what is saying", so the recognition accuracy is improved, and more reliable recognition and verification of safety is realized.
Obviously, the user identification device according to the embodiment of the present specification may be used as the execution subject of the user identification method shown in fig. 2, and thus the functions of the user identification device in fig. 2 can be implemented. Since the principle is the same, the detailed description is omitted here.
Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present specification. Referring to fig. 5, at a hardware level, the electronic device includes a processor, and optionally further includes an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor, the network interface, and the memory may be connected to each other via an internal bus, which may be an ISA (Industry Standard Architecture) bus, a PCI (peripheral component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 5, but this does not indicate only one bus or one type of bus.
And the memory is used for storing programs. In particular, the program may include program code comprising computer operating instructions. The memory may include both memory and non-volatile storage and provides instructions and data to the processor.
Optionally, the processor reads a corresponding computer program from the non-volatile memory into the memory and runs the computer program to form the sampling device of the voiceprint feature on a logic level. The processor is used for executing the program stored in the memory and is specifically used for executing the following operations:
and when the target user performs man-machine interaction, acquiring the voiceprint characteristics and the non-voiceprint biological characteristics of the target user.
And performing identity verification on the target user based on the collected non-voiceprint biological characteristics.
And after the identity authentication is successful, storing the acquired voiceprint characteristics as the reference voiceprint characteristics of the target user into a characteristic library.
Or, the processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs the computer program, and the user identification device is formed on a logic level. The processor is used for executing the program stored in the memory and is specifically used for executing the following operations:
and acquiring voice data provided by a user to be identified.
And extracting the voiceprint characteristics of the user to be recognized from the voice data of the user to be recognized.
Acquiring reference voiceprint characteristics of a target user who reserves a sample in a characteristic library, wherein the reference voiceprint characteristics of the sample reserved in the characteristic library are acquired when the target user performs man-machine interaction, and storing the target user into the characteristic library after the target user successfully performs identity verification based on non-voiceprint biological characteristics, except the voiceprint characteristics, acquired during the man-machine interaction.
And performing approximate matching on the voiceprint features of the user to be recognized and the reference voiceprint features of the target user, and determining whether the object to be recognized is the target user.
The sampling method disclosed in the embodiment shown in fig. 1 or the user identification method disclosed in the embodiment shown in fig. 2 may be applied to or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present specification may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the embodiments of the present specification may be embodied directly in a hardware decoding processor, or in a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
It should be understood that the electronic device of the embodiments of the present specification may implement the functions of the above-described voiceprint feature sampling apparatus in the embodiment shown in fig. 1, or implement the functions of the above-described voiceprint feature-based user identification apparatus in the embodiment shown in fig. 2. Since the principle is the same, the detailed description is omitted here.
Of course, besides the software implementation, the electronic device in this specification does not exclude other implementations, such as logic devices or a combination of software and hardware, and the like, that is, the execution subject of the following processing flow is not limited to each logic unit, and may also be hardware or logic devices.
Furthermore, the present specification embodiments also propose a computer-readable storage medium storing one or more programs, the one or more programs including instructions.
Wherein the above instructions, when executed by a portable electronic device comprising a plurality of application programs, enable the portable electronic device to perform the method of the embodiment shown in fig. 1, and are specifically configured to perform the following steps:
and when the target user performs man-machine interaction, acquiring the voiceprint characteristics and the non-voiceprint biological characteristics of the target user.
And performing identity verification on the target user based on the collected non-voiceprint biological characteristics.
And after the identity authentication is successful, storing the acquired voiceprint characteristics as the reference voiceprint characteristics of the target user into a characteristic library.
Alternatively, the above instructions, when executed by a portable electronic device comprising a plurality of application programs, can cause the portable electronic device to perform the method of the embodiment shown in fig. 2, and is specifically configured to perform the following steps:
and acquiring voice data provided by a user to be identified.
And extracting the voiceprint characteristics of the user to be recognized from the voice data of the user to be recognized.
Acquiring reference voiceprint characteristics of a target user who reserves a sample in a characteristic library, wherein the reference voiceprint characteristics of the sample reserved in the characteristic library are acquired when the target user performs man-machine interaction, and storing the target user into the characteristic library after the target user successfully performs identity verification based on non-voiceprint biological characteristics, except the voiceprint characteristics, acquired during the man-machine interaction.
And performing approximate matching on the voiceprint features of the user to be recognized and the reference voiceprint features of the target user, and determining whether the object to be recognized is the target user.
As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, the description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The above description is only an example of the present specification, and is not intended to limit the present specification. Various modifications and alterations to this description will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification should be included in the scope of the claims of the present specification. Moreover, all other embodiments obtained by a person skilled in the art without making any inventive step shall fall within the scope of protection of this document.
Claims (12)
1. A method of sampling voiceprint features, comprising:
when a target user carries out human-computer interaction, acquiring voiceprint features and non-voiceprint biological features of the target user;
performing identity verification on the target user based on the collected non-voiceprint biological characteristics;
and after the identity authentication is successful, storing the acquired voiceprint characteristics as the reference voiceprint characteristics of the target user into a characteristic library.
2. The method of claim 1, wherein the first and second light sources are selected from the group consisting of,
storing the collected voiceprint features as reference voiceprint features of the target user into a feature library, wherein the step of storing the reference voiceprint features comprises the following steps:
judging whether a feature library stores other reference voiceprint features of the target user in advance;
if so, performing approximate matching on the acquired voiceprint features and other reference voiceprint features of the target user, which are stored in the feature library in advance; and the number of the first and second groups,
and after the approximate matching is successful, storing the acquired voiceprint characteristics as the reference voiceprint characteristics of the target user into a characteristic library.
3. The method of claim 2, wherein the first and second light sources are selected from the group consisting of,
storing the collected voiceprint features as reference voiceprint features of the target user into a feature library, and further comprising:
and if the feature library does not store other reference voiceprint features of the target user in advance, directly storing the voiceprint features of the target user into the feature library as the reference voiceprint features of the target user.
4. The method of claim 2, wherein the first and second light sources are selected from the group consisting of,
storing the collected voiceprint features as reference voiceprint features of the target user into a feature library, and further comprising:
if the approximate matching fails or other reference voiceprint features of the target user are not stored in the feature library in advance, initiating preset deep identity authentication to the target user, wherein the deep identity authentication comprises at least one of password authentication, answer authentication and verification code authentication; and the number of the first and second groups,
and after the deep identity authentication is successful, storing the acquired voiceprint characteristics as the reference voiceprint characteristics of the target user into a characteristic library.
5. The method of any one of claims 1-4,
the non-voiceprint biometric characteristic comprises at least one of a fingerprint characteristic, a palm print characteristic, an iris characteristic, a lip language characteristic, and a face characteristic.
6. A user identification method based on voiceprint features comprises the following steps:
acquiring voice data provided by a user to be identified;
extracting voiceprint characteristics of the user to be recognized from the voice data of the user to be recognized;
acquiring reference voiceprint characteristics of a target user who reserves a sample in a characteristic library, wherein the reference voiceprint characteristics of the sample reserved in the characteristic library are acquired when the target user performs man-machine interaction, and based on non-voiceprint biological characteristics, except the voiceprint characteristics, acquired during the man-machine interaction, of the target user, after the target user is successfully authenticated, the reference voiceprint characteristics are stored in the characteristic library;
and performing approximate matching on the voiceprint features of the user to be recognized and the reference voiceprint features of the target user, and determining whether the object to be recognized is the target user.
7. A voiceprint feature sampling apparatus comprising:
the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring voiceprint characteristics and non-voiceprint biological characteristics except the voiceprint characteristics of a target user when the target user performs man-machine interaction;
the verification module is used for verifying the identity of the target user based on the collected non-voiceprint biological characteristics;
and the sample reserving module is used for storing the acquired voiceprint characteristics serving as the reference voiceprint characteristics of the target user into a characteristic library after the identity authentication is successful.
8. An electronic device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program being executed by the processor to:
when a target user carries out human-computer interaction, acquiring voiceprint characteristics and non-voiceprint biological characteristics except the voiceprint characteristics of the target user;
performing identity verification on the target user based on the collected non-voiceprint biological characteristics;
and after the identity authentication is successful, storing the acquired voiceprint characteristics as the reference voiceprint characteristics of the target user into a characteristic library.
9. A computer-readable storage medium having a computer program stored thereon, which when executed by a processor, performs the steps of:
when a target user carries out human-computer interaction, acquiring voiceprint characteristics and non-voiceprint biological characteristics except the voiceprint characteristics of the target user;
performing identity verification on the target user based on the collected non-voiceprint biological characteristics;
and after the identity authentication is successful, storing the acquired voiceprint characteristics as the reference voiceprint characteristics of the target user into a characteristic library.
10. A user identification device based on voiceprint features, comprising:
the acquisition module acquires voice data provided by a user to be identified;
the extraction module is used for extracting the voiceprint characteristics of the user to be identified from the voice data of the user to be identified;
the sampling module is used for acquiring the reference voiceprint characteristics of a target user who is reserved in a characteristic library, wherein the reference voiceprint characteristics of the reserved sample in the characteristic library are acquired when the target user performs man-machine interaction, and the target user is successfully authenticated and stored in the characteristic library based on the acquired non-voiceprint biological characteristics except the voiceprint characteristics during the man-machine interaction;
and the matching module is used for carrying out approximate matching on the voiceprint characteristics of the user to be recognized and the reference voiceprint characteristics of the target user and determining whether the object to be recognized is the target user.
11. An electronic device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program being executed by the processor to:
acquiring voice data provided by a user to be identified;
extracting voiceprint characteristics of the user to be recognized from the voice data of the user to be recognized;
acquiring reference voiceprint characteristics of a target user who reserves a sample in a characteristic library, wherein the reference voiceprint characteristics of the sample reserved in the characteristic library are acquired when the target user performs man-machine interaction, and based on non-voiceprint biological characteristics, except the voiceprint characteristics, acquired during the man-machine interaction, of the target user, after the target user is successfully authenticated, the reference voiceprint characteristics are stored in the characteristic library;
and performing approximate matching on the voiceprint features of the user to be recognized and the reference voiceprint features of the target user, and determining whether the object to be recognized is the target user.
12. A computer-readable storage medium having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring voice data provided by a user to be identified;
extracting voiceprint characteristics of the user to be recognized from the voice data of the user to be recognized;
acquiring reference voiceprint characteristics of a target user who reserves a sample in a characteristic library, wherein the reference voiceprint characteristics of the sample reserved in the characteristic library are acquired when the target user performs man-machine interaction, and based on non-voiceprint biological characteristics, except the voiceprint characteristics, acquired during the man-machine interaction, of the target user, after the target user is successfully authenticated, the reference voiceprint characteristics are stored in the characteristic library;
and performing approximate matching on the voiceprint features of the user to be recognized and the reference voiceprint features of the target user, and determining whether the object to be recognized is the target user.
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