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WO2018166112A1 - Procédé de vérification d'identité basé sur la reconnaissance d'empreinte vocale, dispositif électronique et support de stockage - Google Patents

Procédé de vérification d'identité basé sur la reconnaissance d'empreinte vocale, dispositif électronique et support de stockage Download PDF

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Publication number
WO2018166112A1
WO2018166112A1 PCT/CN2017/091361 CN2017091361W WO2018166112A1 WO 2018166112 A1 WO2018166112 A1 WO 2018166112A1 CN 2017091361 W CN2017091361 W CN 2017091361W WO 2018166112 A1 WO2018166112 A1 WO 2018166112A1
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WIPO (PCT)
Prior art keywords
voiceprint
voice data
vector
feature vector
gaussian mixture
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PCT/CN2017/091361
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English (en)
Chinese (zh)
Inventor
王健宗
丁涵宇
郭卉
肖京
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平安科技（深圳）有限公司
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Publication of WO2018166112A1 publication Critical patent/WO2018166112A1/fr

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/08—Network architectures or network communication protocols for network security for authentication of entities
    • H04L63/0861—Network architectures or network communication protocols for network security for authentication of entities using biometrical features, e.g. fingerprint, retina-scan
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/26—Speech to text systems
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L17/00—Speaker identification or verification techniques
    • G10L17/02—Preprocessing operations, e.g. segment selection; Pattern representation or modelling, e.g. based on linear discriminant analysis [LDA] or principal components; Feature selection or extraction
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L17/00—Speaker identification or verification techniques
    • G10L17/04—Training, enrolment or model building
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L17/00—Speaker identification or verification techniques
    • G10L17/06—Decision making techniques; Pattern matching strategies
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
    • G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters
    • G10L25/18—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters the extracted parameters being spectral information of each sub-band
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
    • G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters
    • G10L25/24—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters the extracted parameters being the cepstrum

Definitions

  • the present invention relates to the field of communications technologies, and in particular, to a method, an electronic device, and a storage medium for identity verification based on voiceprint recognition.
  • a first aspect of the present invention provides a voiceprint recognition based authentication method, and the voiceprint recognition based identity verification method includes:
  • a second aspect of the present invention provides an electronic device, including a processing device, a storage device, and a voiceprint recognition-based identity verification system, wherein the voiceprint recognition-based identity verification system is stored in the storage device, including at least one computer Reading instructions, the at least one computer readable instruction being executable by the processing device to:
  • a third aspect of the invention provides a computer readable storage medium having stored thereon at least one computer readable instruction executable by a processing device to:
  • the invention has the beneficial effects that the background channel model generated by the pre-training of the present invention is obtained by mining and comparing a large amount of voice data, and the model can accurately describe the user's voice while retaining the user's voiceprint feature to the utmost extent.
  • the background voiceprint feature can be removed at the time of recognition, and the intrinsic feature of the user voice can be extracted, which can greatly improve the accuracy of user identity verification and improve the efficiency of identity verification.
  • FIG. 1 is a schematic diagram of an application environment of a preferred embodiment of a voiceprint recognition based authentication method according to the present invention
  • FIG. 2 is a schematic flow chart of a preferred embodiment of a voiceprint recognition based identity verification method according to the present invention
  • FIG. 3 is a schematic diagram showing the refinement process of step S1 shown in FIG. 2;
  • step S3 is a schematic diagram showing the refinement process of step S3 shown in FIG. 2;
  • FIG. 5 is a schematic structural diagram of a system for authenticating a voiceprint recognition based authentication method according to the present invention.
  • FIG. 1 it is a schematic diagram of an application environment of a preferred embodiment of a method for implementing voiceprint recognition based identity verification according to the present invention.
  • the application environment diagram includes an electronic device 1 and a terminal device 2.
  • the electronic device 1 can perform data interaction with the terminal device 2 through a suitable technology such as a network or a near field communication technology.
  • the terminal device 2 includes, but is not limited to, any electronic product that can interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device, for example, a personal computer, a tablet computer, a smart phone, or an individual.
  • PDA Personal Digital Assistant
  • game console Internet Protocol Television (IPTV)
  • smart wearable device etc.
  • the electronic device 1 is an automatic numerical meter capable of automatically setting according to an instruction set or stored in advance. Equipment for calculation and/or information processing.
  • the electronic device 1 may be a computer, a single network server, a server group composed of multiple network servers, or a cloud-based cloud composed of a large number of hosts or network servers, where cloud computing is a type of distributed computing, A super virtual computer consisting of a loosely coupled set of computers.
  • the electronic device 1 includes, but is not limited to, a storage device 11, a processing device 12, and a network interface 13 that are communicably connected to each other through a system bus. It should be noted that FIG. 1 only shows the electronic device 1 having the components 11-13, but it should be understood that not all illustrated components are required to be implemented, and more or fewer components may be implemented instead.
  • the storage device 11 includes a memory and at least one type of readable storage medium.
  • the memory provides a cache for the operation of the electronic device 1;
  • the readable storage medium may be a non-volatile storage medium such as a flash memory, a hard disk, a multimedia card, a card type memory, or the like.
  • the readable storage medium may be an internal storage unit of the electronic device 1, such as a hard disk of the electronic device 1; in other embodiments, the non-volatile storage medium may also be external to the electronic device 1.
  • a storage device such as a plug-in hard disk equipped with an electronic device 1, a smart memory card (SMC), a Secure Digital (SD) card, a flash card, or the like.
  • SMC smart memory card
  • SD Secure Digital
  • the readable storage medium of the storage device 11 is generally used to store an operating system installed on the electronic device 1 and various types of application software, such as the voiceprint recognition-based identity verification system 10 in an embodiment of the present application. Program code, etc. Further, the storage device 11 can also be used to temporarily store various types of data that have been output or are to be output.
  • Processing device 12 may, in some embodiments, include one or more microprocessors, microcontrollers, digital processors, and the like.
  • the processing device 12 is generally used to control the operation of the electronic device 1, for example, to perform control and processing related to data interaction or communication with the terminal device 2.
  • the processing device 12 is operative to run program code or process data stored in the storage device 11, such as a system 10 that runs voiceprint recognition based authentication.
  • the network interface 13 may comprise a wireless network interface or a wired network interface, which is typically used to establish a communication connection between the electronic device 1 and other electronic devices.
  • the network interface 13 is mainly used to connect the electronic device 1 with one or more terminal devices 2, and establish a data transmission channel and a communication connection between the electronic device 1 and one or more terminal devices 2.
  • the voiceprint recognition based authentication system 10 includes at least one computer readable instructions stored in the storage device 11, the at least one computer readable instructions being executable by the processing device 12 to implement a voiceprint based on embodiments of the present application.
  • the method of identifying the authentication As described later, the at least one computer readable instruction can be classified into different logic modules depending on the functions implemented by its various parts.
  • the voiceprint recognition based authentication system 10 when executed by the processing device 12, the following operations are performed: first, after receiving the voice data of the authenticated user, acquiring the voiceprint feature of the voice data And constructing a corresponding voiceprint feature vector based on the voiceprint feature; then inputting the voiceprint feature vector into a background channel model generated by pre-training to construct a current voiceprint discrimination vector corresponding to the voice data; a spatial distance between the current voiceprint discrimination vector and a pre-stored standard voiceprint discrimination vector of the user, based on the distance Authenticate and generate verification results.
  • FIG. 2 is a schematic flowchart of a method for authenticating a voiceprint recognition based authentication method according to the present invention.
  • the method for authenticating identity based on voiceprint recognition in this embodiment is not limited to the steps shown in the process. Further, in the steps shown in the flowchart, some steps may be omitted, and the order between the steps may be changed.
  • the method for voiceprint recognition based authentication includes the following steps:
  • Step S1 after receiving the voice data of the user who performs the authentication, acquiring the voiceprint feature of the voice data, and constructing a corresponding voiceprint feature vector based on the voiceprint feature;
  • the voice data is collected by the voice collection device (the voice collection device is, for example, a microphone), and the voice collection device sends the collected voice data to the voice recognition-based identity verification system.
  • the voice collection device is, for example, a microphone
  • the voice collection device When collecting voice data, you should try to prevent environmental noise and interference from voice acquisition equipment.
  • the voice collection device maintains an appropriate distance from the user, and tries not to use a large voice acquisition device.
  • the power supply preferably uses the commercial power and keeps the current stable; the sensor should be used when recording the telephone.
  • the voice data may be denoised prior to extracting the voiceprint features in the voice data to further reduce interference.
  • the collected voice data is voice data of a preset data length, or voice data greater than a preset data length.
  • the voiceprint features include various types, such as wide-band voiceprint, narrow-band voiceprint, amplitude voiceprint, etc., and the voiceprint feature of the present embodiment is a Mel Frequency Cepstrum Coefficient (MFCC), which is preferably voice data. .
  • MFCC Mel Frequency Cepstrum Coefficient
  • the voiceprint feature of the voice data is composed into a feature data matrix, which is a voiceprint feature vector of the voice data.
  • Step S2 input the voiceprint feature vector into a background channel model generated by pre-training to construct a current voiceprint discrimination vector corresponding to the voice data;
  • the voiceprint feature vector is input into the background channel model generated by the pre-training.
  • the background channel model is a Gaussian mixture model, and the background channel model is used to calculate the voiceprint feature vector to obtain a corresponding current voiceprint discrimination vector ( I-vector).
  • the calculation process includes:
  • Loglike is a likelihood logarithmic matrix
  • E(X) is a mean matrix trained by a general background channel model
  • D(X) is a covariance matrix
  • X is a data matrix
  • X. 2 is a square of each value of the matrix.
  • Extract the current voiceprint discrimination vector firstly calculate the first-order and second-order coefficients, and the first-order coefficient calculation can be obtained by summing the probability matrix:
  • Gamma i is the i-th element of the first-order coefficient vector
  • loglikes ji is the j-th row of the probability matrix, the i-th element.
  • the second-order coefficients can be obtained by multiplying the transposition of the probability matrix by the data matrix:
  • X Loglike T *feats, where X is a second-order coefficient matrix, loglike is a probability matrix, and feats is a feature data matrix.
  • the primary term and the quadratic term are calculated in parallel, and then the current voiceprint discrimination vector is calculated by the primary term and the quadratic term.
  • the background channel model is a Gaussian mixture model
  • the method includes:
  • the voiceprint feature vector corresponding to each voice data sample is divided into a training set of a first ratio and a verification set of a second ratio, wherein a sum of the first ratio and the second ratio is less than or equal to 1;
  • the Gaussian mixture model is trained by using the voiceprint feature vector in the training set, and after the training is completed, the accuracy of the trained Gaussian mixture model is verified by using the verification set;
  • the model training ends, and the trained Gaussian mixture model is used as the background channel model of the step S2, or if the accuracy is less than or equal to the preset threshold, the voice is added.
  • the number of data samples is re-trained based on the increased speech data samples.
  • the likelihood probability corresponding to the extracted D-dimensional voiceprint feature can be expressed by K Gaussian components:
  • P(x) is the probability that the speech data samples are generated by the Gaussian mixture model (mixed Gaussian model), w k is the weight of each Gaussian model, and p(x
  • K is the number of Gaussian models.
  • the parameters of the entire Gaussian mixture model can be expressed as: ⁇ w i , ⁇ i , ⁇ i ⁇ , w i is the weight of the i-th Gaussian model, ⁇ i is the mean of the i-th Gaussian model, and ⁇ i is the i-th Gaussian
  • Training the Gaussian mixture model can use an unsupervised EM algorithm. After the training is completed, the Gaussian mixture model weight vector, constant vector, N covariance matrix, and the mean multiplied by the covariance matrix are obtained, which is a trained Gaussian mixture model.
  • Step S3 Calculate a spatial distance between the current voiceprint discrimination vector and a pre-stored standard voiceprint discrimination vector of the user, perform identity verification on the user based on the distance, and generate a verification result.
  • the spatial distance of this embodiment is a cosine distance, which is a measure of the magnitude of the difference between two individuals using the cosine of the angles of the two vectors in the vector space.
  • the standard voiceprint discriminant vector is a voiceprint discriminant vector obtained and stored in advance, and the standard voiceprint discriminant vector carries the identifier information of the corresponding user when stored, which can accurately represent the identity of the corresponding user.
  • the stored voiceprint discrimination vector is obtained according to the identification information provided by the user before calculating the spatial distance.
  • the verification passes, and vice versa, the verification fails.
  • the background channel model generated by the pre-training in this embodiment is obtained by mining and comparing a large amount of voice data, and the model can accurately depict the user while maximally retaining the voiceprint features of the user.
  • the background voiceprint feature when speaking, and can remove this feature when identifying, and extracting the inherent features of the user voice, can greatly improve the accuracy of the user identity verification, and improve the efficiency of the identity verification; It makes full use of the voiceprint features related to the vocal vocal in the human voice.
  • This voiceprint feature does not need to limit the text, so it has greater flexibility in the process of identification and verification.
  • the foregoing step S1 includes:
  • Step S11 Perform pre-emphasis, framing, and windowing on the voice data.
  • the voice data is processed.
  • each frame signal is regarded as a stationary signal.
  • the start frame and the end frame of the speech data are discontinuous, and after the framing, the original speech is further deviated. Therefore, the voice data needs to be windowed.
  • Step S12 performing Fourier transform on each window to obtain a corresponding spectrum
  • Step S13 input the spectrum into a mel filter to output a mega spectrum
  • Step S14 performing cepstrum analysis on the Mel spectrum to obtain a Mel frequency cepstral coefficient MFCC, and composing a corresponding voiceprint feature vector based on the Mel frequency cepstral coefficient MFCC.
  • the cepstrum analysis is, for example, taking logarithm and inverse transform.
  • the inverse transform is generally implemented by DCT discrete cosine transform, and the second to thirteenth coefficients after DCT are taken as MFCC coefficients.
  • the Mel frequency cepstrum coefficient MFCC is the voiceprint feature of the speech data of this frame, and the Mel frequency cepstral coefficient MFCC of each frame is composed into a feature data matrix, which is the voiceprint feature vector of the speech data.
  • Step S3 includes:
  • Step S31 calculating a cosine distance between the current voiceprint discrimination vector and the pre-stored standard voiceprint discrimination vector of the user: Identifying the vector for the standard voiceprint, Identify the vector for the current voiceprint;
  • Step S32 if the cosine distance is less than or equal to a preset distance threshold, generating verification pass information
  • Step S33 If the cosine distance is greater than a preset distance threshold, generate information that the verification fails.
  • the foregoing step S3 is replaced by: calculating a spatial distance between the current voiceprint discrimination vector and each of the pre-stored standard voiceprint discrimination vectors, and obtaining The smallest spatial distance, the user is authenticated based on the minimum spatial distance, and a verification result is generated.
  • the difference between the embodiment and the embodiment of FIG. 1 is that the embodiment does not carry the identification information of the user when storing the standard voiceprint authentication vector, and calculates the current voiceprint authentication vector and the pre-stored standard when verifying the identity of the user.
  • the voiceprint discriminates the spatial distance between the vectors and obtains a minimum spatial distance. If the minimum spatial distance is less than a preset distance threshold (the distance threshold is the same as or different from the distance threshold of the above embodiment), the verification passes, otherwise verification failed.
  • FIG. 5 is a functional block diagram of a preferred embodiment of the voiceprint recognition based authentication system 10 of the present invention.
  • the voiceprint recognition based authentication system 10 can be partitioned into one or more modules, one or more modules being stored in a memory and executed by one or more processors to complete this invention.
  • the voiceprint recognition based authentication system 10 can be divided into a detection module 21, an identification module 22, a replication module 23, an installation module 24, and a startup module 25.
  • module refers to a series of computer program instruction segments capable of performing a specific function, which is more suitable than the program for describing the execution of the voiceprint recognition based authentication system 10 in an electronic device, wherein:
  • the first obtaining module 101 is configured to acquire a voiceprint feature of the voice data after receiving the voice data of the user who performs the identity verification, and construct a corresponding voiceprint feature vector based on the voiceprint feature;
  • the voice data is collected by the voice collection device (the voice collection device is, for example, a microphone), and the voice collection device sends the collected voice data to the voice recognition-based identity verification system.
  • the voice collection device is, for example, a microphone
  • the voice collection device When collecting voice data, you should try to prevent environmental noise and interference from voice acquisition equipment.
  • the voice collection device maintains an appropriate distance from the user, and tries not to use a large voice acquisition device.
  • the power supply preferably uses the commercial power and keeps the current stable; the sensor should be used when recording the telephone.
  • the voice data may be denoised prior to extracting the voiceprint features in the voice data to further reduce interference.
  • the collected voice data is a preset number. According to the length of the voice data, or the voice data is greater than the preset data length.
  • the voiceprint features include various types, such as wide-band voiceprint, narrow-band voiceprint, amplitude voiceprint, etc., and the voiceprint feature of the present embodiment is a Mel Frequency Cepstrum Coefficient (MFCC), which is preferably voice data. .
  • MFCC Mel Frequency Cepstrum Coefficient
  • the voiceprint feature of the voice data is composed into a feature data matrix, which is a voiceprint feature vector of the voice data.
  • the constructing module 102 is configured to input the voiceprint feature vector into a background channel model generated by pre-training to construct a current voiceprint discrimination vector corresponding to the voice data;
  • the voiceprint feature vector is input into the background channel model generated by the pre-training.
  • the background channel model is a Gaussian mixture model, and the background channel model is used to calculate the voiceprint feature vector to obtain a corresponding current voiceprint discrimination vector ( I-vector).
  • the calculation process includes:
  • Loglike is a likelihood logarithmic matrix
  • E(X) is a mean matrix trained by a general background channel model
  • D(X) is a covariance matrix
  • X is a data matrix
  • X. 2 is a square of each value of the matrix.
  • Extract the current voiceprint discrimination vector firstly calculate the first-order and second-order coefficients, and the first-order coefficient calculation can be obtained by summing the probability matrix:
  • Gamma i is the i-th element of the first-order coefficient vector
  • loglikes ji is the j-th row of the probability matrix, the i-th element.
  • the second-order coefficients can be obtained by multiplying the transposition of the probability matrix by the data matrix:
  • X Loglike T *feats, where X is a second-order coefficient matrix, loglike is a probability matrix, and feats is a feature data matrix.
  • the primary term and the quadratic term are calculated in parallel, and then the current voiceprint discrimination vector is calculated by the primary term and the quadratic term.
  • the background channel model is a Gaussian mixture model
  • the voiceprint recognition based authentication system further comprises:
  • a second acquiring module configured to acquire a preset number of voice data samples, and obtain each voice data a voiceprint feature corresponding to the sample, and constructing a voiceprint feature vector corresponding to each voice data sample based on the voiceprint feature corresponding to each voice data sample;
  • a dividing module configured to divide the voiceprint feature vector corresponding to each voice data sample into a training set of a first ratio and a verification set of a second ratio, wherein a sum of the first ratio and the second ratio is less than or equal to 1;
  • a training module is configured to train the Gaussian mixture model by using the voiceprint feature vector in the training set, and after the training is completed, verify the accuracy of the trained Gaussian mixture model by using the verification set;
  • a processing module if the accuracy is greater than a preset threshold, the model training ends, the trained Gaussian mixture model is used as the background channel model, or, if the accuracy is less than or equal to a preset threshold, The number of speech data samples is described and re-trained based on the increased speech data samples.
  • the likelihood probability corresponding to the extracted D-dimensional voiceprint feature can be expressed by K Gaussian components:
  • P(x) is the probability that the speech data samples are generated by the Gaussian mixture model (mixed Gaussian model), w k is the weight of each Gaussian model, and p(x
  • K is the number of Gaussian models.
  • the parameters of the entire Gaussian mixture model can be expressed as: ⁇ w i , ⁇ i , ⁇ i ⁇ , w i is the weight of the i-th Gaussian model, ⁇ i is the mean of the i-th Gaussian model, and ⁇ i is the i-th Gaussian
  • Training the Gaussian mixture model can use an unsupervised EM algorithm. After the training is completed, the Gaussian mixture model weight vector, constant vector, N covariance matrix, and the mean multiplied by the covariance matrix are obtained, which is a trained Gaussian mixture model.
  • the first verification module 103 is configured to calculate a spatial distance between the current voiceprint discrimination vector and the pre-stored standard voiceprint discrimination vector of the user, perform identity verification on the user based on the distance, and generate a verification result.
  • the spatial distance of the present embodiment is a cosine distance
  • the cosine distance is a cosine value of the angle between two vectors in the vector space.
  • the standard voiceprint discriminant vector is a voiceprint discriminant vector obtained and stored in advance, and the standard voiceprint discriminant vector carries the identifier information of the corresponding user when stored, which can accurately represent the identity of the corresponding user.
  • the stored voiceprint discrimination vector is obtained according to the identification information provided by the user before calculating the spatial distance.
  • the verification passes, and vice versa, the verification fails.
  • the first acquiring module 101 is specifically configured to perform pre-emphasis, framing, and windowing on the voice data; Fourier transform to obtain the corresponding spectrum; input the spectrum into the Meyer filter to output the Mel spectrum; perform cepstrum analysis on the Mel spectrum to obtain the Mel frequency cepstral coefficient MFCC, A corresponding voiceprint feature vector is formed based on the Mel frequency cepstral coefficient MFCC.
  • the pre-emphasis processing is actually a high-pass filtering process, filtering out the low-frequency data, so that the high-frequency characteristics in the speech data are more prominent.
  • N frames short-time signals
  • there is a repeating area between adjacent frames and the repeating area is generally 1/2 of the length of each frame; after the framed speech data, each frame signal is regarded as a stationary signal.
  • the voice data needs to be windowed.
  • the cepstrum analysis is, for example, taking logarithm and inverse transform.
  • the inverse transform is generally implemented by DCT discrete cosine transform, and the second to thirteenth coefficients after DCT are taken as MFCC coefficients.
  • the Mel frequency cepstrum coefficient MFCC is the voiceprint feature of the speech data of this frame, and the Mel frequency cepstral coefficient MFCC of each frame is composed into a feature data matrix, which is the voiceprint feature vector of the speech data.
  • the first verification module 103 is specifically configured to calculate between the current voiceprint discrimination vector and the pre-stored standard voiceprint discrimination vector of the user.
  • Cosine distance Identifying the vector for the standard voiceprint, And identifying the vector for the current voiceprint; if the cosine distance is less than or equal to the preset distance threshold, generating information for verifying the pass; if the cosine distance is greater than the preset distance threshold, generating information that the verification fails.
  • the first verification module is replaced by a second verification module, configured to calculate the current voiceprint discrimination vector and pre-stored standard voiceprint identification.
  • the spatial distance between the vectors, the minimum spatial distance is obtained, the user is authenticated based on the minimum spatial distance, and a verification result is generated.
  • the present embodiment does not carry the identification information of the user when storing the standard voiceprint authentication vector, and calculates the current voiceprint authentication vector and the pre-stored standard when verifying the identity of the user.
  • the voiceprint discriminates the spatial distance between the vectors and obtains a minimum spatial distance. If the minimum spatial distance is less than a preset distance threshold (the distance threshold is the same as or different from the distance threshold of the above embodiment), the verification passes, otherwise verification failed.

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Abstract

La présente invention concerne un procédé de vérification d'identité basé sur la reconnaissance d'empreinte vocale, un dispositif électronique et un support de stockage. Le procédé de vérification d'identité basé sur la reconnaissance d'empreinte vocale comprend : après réception de données vocales d'un utilisateur procédant à une vérification d'identité, l'obtention d'une caractéristique d'empreinte vocale des données vocales, et la construction d'un vecteur de caractéristique d'empreinte vocale correspondant sur la base de la caractéristique d'empreinte vocale ; l'entrée du vecteur de caractéristique d'empreinte vocale dans un modèle de canal d'arrière-plan généré en apprenant à l'avance à construire un vecteur d'identification d'empreinte vocale actuel correspondant aux données vocales ; et le calcul d'une distance d'espacement entre le vecteur d'identification d'empreinte vocale actuel et un vecteur d'identification d'empreinte vocale standard pré-stocké de l'utilisateur, la réalisation d'une vérification d'identité relative à l'utilisateur sur la base de la distance, et la génération d'un résultat de vérification. La présente invention peut améliorer la précision et l'efficacité de la vérification de l'identité d'un utilisateur.
PCT/CN2017/091361 2017-03-13 2017-06-30 Procédé de vérification d'identité basé sur la reconnaissance d'empreinte vocale, dispositif électronique et support de stockage WO2018166112A1 (fr)

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