CN108550368A - A kind of processing method of voice data - Google Patents

A kind of processing method of voice data Download PDF

Info

Publication number
CN108550368A
CN108550368A CN201810225485.2A CN201810225485A CN108550368A CN 108550368 A CN108550368 A CN 108550368A CN 201810225485 A CN201810225485 A CN 201810225485A CN 108550368 A CN108550368 A CN 108550368A
Authority
CN
China
Prior art keywords
pos machine
user
intelligent pos
payment platform
voice
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN201810225485.2A
Other languages
Chinese (zh)
Other versions
CN108550368B (en
Inventor
李仁超
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guangzhou Helipay Payment Technology Co ltd
Original Assignee
Chengdu Xinda Outwit Technology Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Chengdu Xinda Outwit Technology Co Ltd filed Critical Chengdu Xinda Outwit Technology Co Ltd
Priority to CN201810225485.2A priority Critical patent/CN108550368B/en
Publication of CN108550368A publication Critical patent/CN108550368A/en
Application granted granted Critical
Publication of CN108550368B publication Critical patent/CN108550368B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/08—Payment architectures
    • G06Q20/20—Point-of-sale [POS] network systems
    • G06Q20/206—Point-of-sale [POS] network systems comprising security or operator identification provisions, e.g. password entry
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/38—Payment protocols; Details thereof
    • G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
    • G06Q20/401—Transaction verification
    • G06Q20/4014—Identity check for transactions
    • G06Q20/40145—Biometric identity checks
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L17/00—Speaker identification or verification
    • G10L17/22—Interactive procedures; Man-machine interfaces
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
    • H04L9/32—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials
    • H04L9/3247—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials involving digital signatures
    • H04L9/3249—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials involving digital signatures using RSA or related signature schemes, e.g. Rabin scheme

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Computer Security & Cryptography (AREA)
  • Accounting & Taxation (AREA)
  • Physics & Mathematics (AREA)
  • Finance (AREA)
  • General Physics & Mathematics (AREA)
  • Acoustics & Sound (AREA)
  • Theoretical Computer Science (AREA)
  • Multimedia (AREA)
  • Human Computer Interaction (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Health & Medical Sciences (AREA)
  • Strategic Management (AREA)
  • General Business, Economics & Management (AREA)
  • Signal Processing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Cash Registers Or Receiving Machines (AREA)
  • Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)

Abstract

The present invention provides a kind of processing method of voice data, this method includes：Intelligent POS machine and payment platform are established by safe lane and connected；Intelligent POS machine client carries out speech recognition to user；Subscriber authentication is carried out based on voice recognition result.The present invention proposes a kind of processing method of voice data, realizes intelligent POS machine terminal identity authentication data and is locally stored, compares and operation, need not configure hardware encryption equipment, without being uploaded to payment platform, more safety.

Description

A kind of processing method of voice data
Technical field
The present invention relates to speech recognition, more particularly to a kind of processing method of voice data.
Background technology
The safety of the especially intelligent POS machine of the network security of point-of-sale terminal attracts people's attention at present, by intelligent POS machine into The safety problem that row information is transmitted is increasingly subject to the attention of people.All it is to use user in current intelligent POS machine application The user authentication of name, password, and to intelligent POS machine user issuing digital certificate, and can not using hardware encryption terminal secret key Export property, reinforces the identity security of user.But the hardware encryption equipment of any form need to all be set in the external entity of intelligent POS machine It is standby, the ease for use of scheme is more reduced, the operation complexity of user is increased.For the fingerprint recognition of the prior art, identification letter Breath needs to transmit, and safety is challenged.If the feature database of payment platform storage loses, identity can not be carried out and recognized Card.
Invention content
To solve the problems of above-mentioned prior art, the present invention proposes a kind of processing method of voice data, packet It includes：
Intelligent POS machine and payment platform are established by safe lane and connected；
Intelligent POS machine client carries out speech recognition to user；
Subscriber authentication is carried out based on voice recognition result.
Preferably, the intelligent POS machine client carries out speech recognition to user, further comprises：
Intelligent POS machine obtains the speech recognition request pre-actuated from payment platform；
The identification method currently supported based on intelligent POS machine judges whether the intelligent POS machine supports the speech recognition.
Preferably, if judging result is to support speech recognition, intelligent POS machine client utilizes the identification knot of user speech Fruit carries out subscriber authentication；
Alternatively,
According to the authentication mode that the certification request of startup and current intelligent POS machine are supported, filtering out current intelligent POS machine can Certification is presented to user, selects and verifies for user.
Preferably, the RSA key generated in security context using the authentication module of intelligence POS machine when starting speech recognition Random number is encrypted in the private key for user of centering, and secret value is returned to payment platform；
Payment platform verifies the validity of secret value using the client public key stored after speech recognition is started；
After intelligent POS machine authentication module completes authentication, in the next use when intelligent POS machine, credible deposit is directly invoked Abstract is encrypted in the private key for the RSA key centering stored in storage block, and secret value is transmitted to payment platform and is verified.
The present invention compared with prior art, has the following advantages：
The present invention proposes a kind of processing method of voice data, realizes intelligent POS machine terminal identity authentication data sheet Ground storage compares and operation, hardware encryption equipment need not be configured, without being uploaded to payment platform, more safety.
Description of the drawings
Fig. 1 is a kind of flow chart of the processing method of voice data according to the ... of the embodiment of the present invention.
Specific implementation mode
Retouching in detail to one or more embodiment of the invention is hereafter provided together with the attached drawing of the diagram principle of the invention It states.The present invention is described in conjunction with such embodiment, but the present invention is not limited to any embodiments.The scope of the present invention is only by right Claim limits, and the present invention covers many replacements, modification and equivalent.Illustrate in the following description many details with Just it provides a thorough understanding of the present invention.These details are provided for exemplary purposes, and without in these details Some or all details can also realize the present invention according to claims.
An aspect of of the present present invention provides a kind of processing method of voice data.Fig. 1 is according to the ... of the embodiment of the present invention one The process flow figure of kind voice data.
The intelligent POS machine of the present invention is connect with payment platform by safe lane.Intelligent POS machine obtains pre- from payment platform The speech recognition request first started.The identification method currently supported based on intelligent POS machine judges whether the intelligent POS machine is supported The speech recognition.
If supporting the speech recognition, intelligent POS machine client carries out user identity using the recognition result of user speech Verification.
If being verified, random number is encrypted in the private key of the RSA key centering generated when being started using authentication, The first secret value is obtained, and first secret value is sent to the payment platform by intelligent POS machine client, for institute It states the client public key obtained when payment platform is started based on first secret value and authentication and carries out authentication.
During authenticating user identification, intelligent POS machine is downloaded the certification that current intelligent POS machine starts by payment platform and is asked Ask, and the identification method that current intelligent POS machine is supported found using intelligent POS machine client, according to the certification request of startup and The authentication mode that current intelligence POS machine is supported filters out the current intelligent available certification of POS machine and is presented to user, is selected for user And verification.
After user's checking, using the RSA that is generated in security context of authentication module of intelligence POS machine when starting speech recognition Random number is encrypted in the private key for user of cipher key pair, and secret value is returned to payment platform.Payment platform utilizes startup language The validity of the client public key verification secret value stored after sound identification.
After obtaining secret value, whether effectively judge whether authentication succeeds according to secret value, if secret value has Effect, authentication success；If secret value is invalid, authentication failure.
Before starting certification, need to negotiate identification method, specific authentication starting mistake with payment platform using intelligent POS machine Journey includes：
Intelligent POS machine obtains the identification method negotiated from the payment platform；Enumerate the identification that intelligent POS machine is currently supported Mode, judges whether the intelligent POS machine supports speech recognition；
If supporting, intelligent POS machine client carries out subscriber authentication using speech recognition；If subscriber authentication is logical It crosses, authentication module generates RSA key pair in security context, and using the authentication module private key in intelligent POS machine to the RSA The client public key of cipher key pair is encrypted, and generates the second secret value；
Then, the client public key of second secret value and authenticated module private key encryption is passed through intelligence by authentication module POS machine client is uploaded to payment platform, so that whether payment platform uses the second secret value described in authentication module public key verifications Effectively.
During being somebody's turn to do, the identification method that current intelligent POS machine is supported is found using intelligent POS machine client, according to current The identification method that intelligent POS machine is supported filters out available certification and is presented to user, after user's checking, the certification of intelligent POS machine Module generates RSA key, and public key and the certification request return authentication management platform of startup are stored.
After starting speech recognition, RSA key pair is generated in the trusted storage block of intelligent POS machine, and by RSA key pair In client public key export, by Encryption Transmission Protocol, client public key is transmitted to payment platform.Intelligence POS in the next use When machine, after authentication module completes authentication, the private key for the RSA key centering stored in trusted storage block is directly invoked to abstract It is encrypted, and secret value is transmitted to payment platform and is verified.
The speech recognition request wherein sent by the interface intelligence POS machine client of the trusted storage block, and root According to the identification request received, corresponding identification process is created, and by executing the identification process, manage the authentication module And voice acquisition module completes the identification process jointly.
Specifically, sent first by the interface intelligence POS machine client of the trusted storage block when payment platform When speech recognition request, payment platform creates identification process, and by executing the identification process according to the speech recognition request, Call instruction is sent to the authentication module.
Secondly, the authentication module is after the call instruction for receiving payment platform transmission, according to the call instruction, determine to Payment platform returns to the acquisition instructions for calling the voice acquisition module.So that payment platform is according to the acquisition instructions, to the language Sound acquisition module forwards the acquisition instructions.
Later, the acquisition instructions that voice acquisition module is forwarded according to payment platform pass through the interface tune of the trusted storage block The sound bite is acquired with the voice-input device of intelligent POS machine, and collected sound bite is returned by payment platform The authentication module.
The authentication module receives the sound bite of the voice acquisition module acquisition of payment platform forwarding.If payment platform is sent Call instruction in carry the identity information to be identified, then the authentication module can create the sound bite and this is to be identified The incidence relation of identity information, and using the sound bite and the identity information to be identified voice messaging to be identified as this Return to payment platform.Alternatively, the authentication module according to preset algorithm, extracts the corresponding user's language to be identified of the sound bite Sound feature templates, then the incidence relation of the user vocal feature template and the identity information to be identified to be identified is created, and The user vocal feature template to be identified and the identity information to be identified voice messaging to be identified as this are returned Payment platform.
And when not carrying the identity information to be identified in the call instruction that payment platform is sent, then the authentication module can The sound bite is directly returned into the user vocal feature mould to be identified that payment platform or the authentication module can will extract Plate returns to payment platform.Payment platform receives the sound bite to be identified or the user vocal feature template to be identified, When payment platform receives the voice messaging to be identified, payment platform is according to scheduled safety is advised in advance with payment platform Then, intelligent POS machine client is returned to by the interface of the trusted storage block after which being encrypted, when When payment platform receives the sound bite or the user vocal feature template to be identified, payment platform can be according to the tune With business, corresponding identity information to be identified is determined, and then determine the voice messaging to be identified, and to be identified to this Voice messaging by the interface of the trusted storage block returns to intelligent POS machine client after being encrypted.
In the preferred embodiment of the invention, the intelligence POS machine client is carried out using the recognition result of user speech Subscriber authentication further includes：The voice of input is verified, is the user of login bank card reading program after being verified ID generates a pair of public and private key, and private key is stored securely in the trusted storage block of intelligent POS machine；Use the trusted storage of intelligent POS machine Terminal secret key built in block carries out the public key of User ID and the characteristic sequence of the voice of the User ID and login user ID Encryption；
Wherein, terminal secret key is preset in the secure storage section of equipment when intelligent POS machine is dispatched from the factory；Every POS The public private key pair of machine has uniqueness；
It is that the characteristic sequence of the voice is encrypted when the voice to login user ID is encrypted, voice messaging Characteristic sequence is generated in the trusted storage block storage of intelligent POS machine, characteristic sequence create-rule can be according to any suitable Audio database search rule generates, and the corresponding sound bite of characteristic sequence has uniqueness.
Will include by the encrypted public key of terminal secret key, the User ID and the phonetic feature sequence as certification Request is sent to payment platform, so that payment platform verifies the public key after receiving the certification request, and preserves The public key, the User ID and the phonetic feature sequence.
The terminal secret key is preset in the secure storage section of equipment in the trusted storage block manufacture of intelligent POS machine , then the terminal public key in payment platform can be sent to payment platform storage in advance by intelligent POS machine terminal or payment is flat It directly stores in platform, is identified by equipment unique identifier between a pair of of terminal public key and terminal secret key；
After payment platform receives certification request, the information for including in certification request is by the terminal of intelligent POS machine terminal Private key encryption, thus payment platform retrieves terminal public key corresponding with the terminal secret key by the encryption information, completes verification； After being verified by certification request the public key and User ID and phonetic feature sequence store；Payment platform is fed back Recognition result to intelligent POS machine trusted storage block.
After the completion of registration, when logging on bank card reading program with registered above-mentioned User ID, input voice carries out Verification operation；By the User ID private key that is stored in the trusted storage block of intelligent POS machine to the feature of User ID and the voice Sequence is encrypted.
Certification request comprising User ID and phonetic feature sequence is sent to payment platform, so that payment platform reception is recognized It is verified after card request, and checks that whether corresponding with User ID when registration the voice of the phonetic feature sequence in certification request be special Sign sequence is consistent to obtain authentication result.
If certification does not pass through, the trusted storage block of intelligent POS machine initiates re-authentication request, and payment platform can not will have It is added in authentication record by the phonetic feature sequence of certification, certification is that this has with phonetic feature sequence inconsistent when registration The standby permission serviced using bank card reading program.
For the phonetic feature sequence inconsistent with the phonetic feature sequence in certification request, if recognizing again in initiation The execution identifying code that can execute bank card reading program service is provided in card request, then by the phonetic feature in certification request Sequence is stored in authentication record, completes authentication.
Before carrying out match cognization to voice, need to carry out preemphasis, filtering, adding window framing, end-point detection.With work(in short-term Rate and ZCR make that feature differentiation is mute and voice.First thresholding is determined for short-time rating and ZCR, then Continuous plus is in short-term before detection Power and ZCR adjust thresholding, carry out state analysis, judge whether mute section terminated.
In end-point detection, frequency band is divided into 4 sections, calculates the power ratio SE of subband according to the following formula：
Wherein：UiAnd LiThe upper limiting frequency and lower frequency limit of expression subband i respectively, i=1,2,3,4；X (ω) indicates signal Amplitude at frequencies omega.
If the power ZCR of certain frame signal is less than thresholding, and the SE approximately equals of 4 cross-talk bands, then judge to be mute section.
Preferably, the detection of speech sound signal terminal point is realized using neural network combination particle cluster algorithm：
1：If one-dimensional neural network hidden node includes K × L θ and K λ, export node layer K × N number of θ for being included with N number of λ, wherein K are hidden node number, and L is the number of input node, and N is output layer node number, and θ and λ are respectively phase rotated Coefficient and phase controlling factor；Population and one-dimensional neural network relevant parameter are initialized；
2：One section of signal containing voice segments and noise is randomly choosed, by short-time rating, circular average magnitude difference function, frequency band Variance is inputted as one-dimensional neural network, is indicated output of every frame signal start-stop as one-dimensional neural network, is completed one-dimensional god It is built through training sample；
3：Training sample is inputted one-dimensional neural network to be trained, by population to one-dimensional Neural Network Optimization, is made One-dimensional neural network output reaches with idea output is pre-designed requirement, and then completes one-dimensional neural metwork training；One-dimensional god It is as follows through the specific Optimization Steps of network parameter：
1) the equity parameter to be optimized with study is initialized；The movement position of optimization particle and velocity vector are set Count into matrix, wherein row indicates that each parameter to be learned, row indicate optimization Motion Particles；
2) output of entire one-dimensional neural network is calculated | Y>It is as follows to define fitness function by n：
|O>N indicates the expression of n-th of output neuron target output, | Y>N is n-th of output neuron reality output Expression；
3) the current speed of each particle and position are updated by the speed of population and location formula；Particle i works as Preceding speed update is simplified as：
vt+1 i=vt i+c1r2-c2xt i
The update of the current locations particle i is simplified as：
xt+1 i=xt i+vt+1 i
r1And r2For the independent random number between [0,1], c1And c2Indicate that acceleration limits factor, wherein c1For adjusting The step-length that section particle is advanced to respective optimal location, c2The step-length advanced to global particle optimal location for adjusting individual.
4) fitness for calculating and evaluating each particle, the extreme value to more new individual and global extreme value；
5) when meeting termination condition to get to the optimal value of the parameter θ of the hidden layer of one-dimensional neural network and output layer, λ； Then, then by the parameter store, optimization process terminates；Otherwise it goes to and 3) continues to search for；
After neural metwork training, original training sample is calculated with trained one-dimensional neural network, and defeated Go out testing result, if output result is more than threshold value, then it is assumed that present frame is speech frame, is otherwise non-speech frame, then will be real Border output result is compared with the signal speech frame indicated, if one-dimensional neural metwork training is ineffective, it is right to need again It is trained；
Carry out speech terminals detection；One section of voice signal is taken, its characteristic quantity is extracted, then uses trained one-dimensional nerve Network is detected it, final output speech terminals detection result.
After completing end-point detection, voice signal is divided into R isometric non-overlapping frames, is denoted as fk={ fk(n) | n=1, 2 ..., L/R；K=1,2 ..., R }, wherein：L is voice signal length；R is totalframes；fk(n) it is n-th of sampling of kth frame Value.
Short Time Fourier Transform is carried out to every frame signal after pretreatment, marks off subband according to the following formula：
Bi=exp [(lgFmin+i(lgFmax-lgFmin)/M)]
Wherein：I indicates subband number, value 1,2,3 ..., M；M indicates number of sub-bands；Fmin、FmaxFor sense of hearing bandwidth The bandwidth range of lower and upper limit, subband i is [Bi- 1, Bi].Subband power B is calculated on each subbandi, obtain M subband work( Rate.
The dynamic change of audio power is calculated by the power difference between consecutive frame and its adjacent sub-bands：
E(k)n=e (k)n+1-e(k)n,
dE(k)n=E (k+1)n-E(k)n,
If dE (k)n≤ 0, F (k)n=0,
If dE (k)n＞ 0, F (k)n=1,
Wherein：N=0,1,2 ..., M-1 indicate subband number；K indicates frame number.
I.e. first to adjacent sub-bands rate of doing work difference E (k)n, then difference dE (k) is asked to the differential power of consecutive framen, carry out Threshold decision, to obtain feature F (k)n。
By frequency range [0, fs/ 2] it is divided into N number of subband, calculates the center of gravity of m-th of subband：
Wherein：lm、hmFor the lower frequency limit and upper limiting frequency of subband；P (f) is the band power at f；
Then regularizing filter makes its value not influenced by subband selection, as follows with center of gravity again：
NCm=[Cm-(hm+lm)]/2(hm-lm)。
Wherein, NCmFor regularizing filter band center of gravity.
Using parametrization hash index table, former list item is mapped on hash index table, gives fingerprint F (k)nGet Ha Xisuo Draw value：
H(F(k)n)=F (k)nMaxlen
Wherein：Maxlen is hash index table size；H(F(k)n) it is hash index value, numerical value is in 0~Maxlen-1；
Calculate kth frame voice signal fk(n) power ratio of every frame is calculated in ZCR in short-term：
Ck=Bk/(Rk+ b),
B is anti-spilled constant, R in formulakFor the ZCR in short-term of kth frame；
Obtain the vector H={ H (F (k) of power ration)Ck| k=1,2 ..., R }.
Next out of order encryption is used to Hash sequence H.Firstly generate the pseudo-random sequence S with Hash sequence equal length =[s1, s2..., sR], then Hash sequence carries out position rearrangement reaction according to the value of pseudo-random number sequence, and encrypted sequence is h* (si)=h (i),
Wherein：Only as H (i)>When H (i-1), h (i) is 1, and otherwise h (i) is 0.
During voice authentication, using the similitude of blackman distance metric voice signals, for two audio pieces Section θ1And θ2, h1It is denoted as voice signal θ1Hash index value, h2It is denoted as voice signal θ2Hash index value.D is denoted as h1And h2's Regularization blackman distance D, the i.e. ratio of hash index value mistake digit and total bit, calculation formula are：
If two audio fragment θ1And θ2Feature it is identical, thenIf two audio fragment θ1And θ2Spy Sign differs, thenWhereinTo identify certification threshold value.If distanceThen think two audio section θ1With θ2Feature it is identical, certification passes through；Otherwise certification does not pass through.
In an additional preferred embodiment, non-registered users can also carry out payment platform by random phonetic characters string Registration.Specifically, payment platform generates random string and is sent to intelligent POS machine user；The random string that user will receive Voice is recorded into, voice is sent to payment platform；After payment platform receives the voice of user, the MFCC for extracting voice is special Sign；
According to the MFCC features of voice, character string text is converted speech into, if obtained character string text and pre- Mr. At random string content it is identical, then by this section of phonetic symbol be active registration voice；Otherwise it is labeled as invalid voice；
Correspondingly, in Qualify Phase：When intelligent POS machine user sends out ID authentication request, payment platform firstly generates Random string is sent to user, and user will record after sequence that the random string received is specified according to payment platform, Certification voice is obtained, the certification voice generated is sent to payment platform；If user fails typing language within certain duration Sound, then current random string failure, user's checking failure；
After payment platform receives certification voice, the MFCC features of certification voice are extracted；The user for verifying the certification voice is special Sign whether belongs to active user and whether content is originally consistent with correct characters illustration and text juxtaposed setting, respectively obtains voice match value S1With text With value S2；
By voice match value S1With text matches value S2Final score value is obtained after weighted sum, is relatively gone forward side by side with given threshold Row judgement：When final score value is more than given threshold, then it is assumed that registered users and language of the certification voice from intelligent POS machine The content of text of sound is correct, is verified；Otherwise authentication failed；
The calculating of final score value is as follows：
S=wS1+(1-w)S2
In formula, S is final score value, and w is weight, 0<w<1
Wherein, whether the user characteristics of the above-mentioned verification certification voice belong to active user and content and correct characters illustration and text juxtaposed setting Whether this is consistent, and further comprises：
According to the first HMM of sequential build of correct characters illustration and text juxtaposed setting sheet；
According to the MFCC features and the first HMM of certification voice, the MFCC that certification voice is obtained using Viterbi algorithm is special Mapping between sign and the first HMM states so that：
Φ*t=argmaxΦp(Xt|H,Φt)
In formula, XtFor the MFCC characteristic sets { x of certification voicet(1), xt(2) ..., xt(Nt), NtIt is special for certification voice Total quantity is levied, subscript t represents certification voice segments, and H is the first HMM, ΦtFor the mapping of certification voice MFCC features and HMM states, p(Xt| H, Φt) indicate certification voice MFCC characteristic sets XtIn the first HMM and state corresponded manner ΦtUnder whole likelihood Probability, Φ *tFor the optimal mapping between the Viterbi algorithm certification voice MFCC features found and the first HMM states；
According to the mapping between the MFCC features and the first HMM states of certification voice, and then it is special to obtain certification voice MFCC The mapping of sign and each character calculates certification voice in specific user's voice GMM model and the log-likelihood of general GMM model Than as voice match value S1；Voice match value S1Calculation expression it is as follows：
In formula, xt(n) it is the n-th frame MFCC features of certification voice, Nt 1Indicate that all character texts correspond in certification voice MFCC feature quantities, d (n) be correct characters illustration and text juxtaposed setting sheet under the conditions of the corresponding character of certification voice n-th frame MFCC features, Λ0 d(n)And Λd(n)The corresponding specific user's GMM models of respectively character d (n) and general GMM model, p (xt(n)|Λ0 d(n)And p (xt(n)|Λd(n)) it is respectively whole likelihood probabilities of the xt (n) in two kinds of GMM models；
The string content for identifying certification voice, the string content that verification is obtained is as optimal character string；According to most Excellent character string builds the 2nd HMM using general GMM model；
Mapping between the MFCC features of certification voice and the 2nd HMM states is obtained using Viterbi algorithm, and then is obtained The mapping of certification voice MFCC features and each character；
According to respectively obtain under correct characters illustration and text juxtaposed setting sheet and optimal character string certification voice MFCC features with it is each The mapping of character calculates certification voice specific user's voice GMM model and the log-likelihood ratio on general GMM model, as text This matching value S2；Text matches value S2Calculation expression it is as follows：
In formula,Indicate the corresponding MFCC feature quantities of optimal character text, d in certification voice2(n) it is optimal character string Under the conditions of the corresponding character of certification voice n-th frame MFCC features,For d2(n) corresponding general GMM model,For xt(n) in d2(n) the whole likelihood probability on general GMM model.
To eliminate the unmatched influence of channel, when estimating user's identification model, space and channel space are identified in user It is modeled simultaneously based on factor analysis.One section of voice is indicated by a composite vector, i.e., speech space can by user and The composite vector of channel forms.
Composite vector M is indicated with following formula：
M=s+c
S=m+Vy+Dz
C=Ux
Wherein s is user characteristics space vector, and c is channel space vector, and m is general GMM vectors, and V, D and U are spatial moments Battle array.The component of vector x identifies that factor, the component of z are known as residual error factor as channel factor, the component of y as user.By estimating The matrix in space is counted, user's identification model is established and tests into factor analytic process.
In space matrix estimation procedure, a voice output user and speech feature vector { x are given1, x2..., xTCan ：
Wherein mcRepresent the mean value subvector of channel c, xt, γt(c) be each GMM function state probability, Nc(s), Fc (s), Sc(s) it is respectively zeroth order, the statistics of single order, second order of the user s on c-th of GMM.
Later, above-mentioned statistic is spliced：Nc(s) the diagonal matrix N (s) of CF × CF, F are spliced intoc(s) it is spliced into CF × 1, column vector F (s), Sc(s) CF × CF diagonal matrix S (s) are spliced into, CF is the dimension of general GMM vectors.
The intermediate variable of each user is calculated again：
L (s)=VTΨ-1N (s) V,
Wherein Ψ is the covariance matrix of general GMM；
The single order and second order desired value that user identifies factor y (s) are calculated using L (s)：
E [y (s)]=L-1(s)VTΨ-1F (s),
E[y(s)y-1(s)]=E [y (s)] E [yT(s)]+L-1(s)
N (s), F (s), S (s) are respectively the zeroth order of user s feature space vectors, the statistic of single order, second order；
It updates user and identifies space matrix V and covariance matrix Ψ s
V=ΨsF(s)E[yT(s)]/(∑sN(s)E[y(s)yT(s)]),
Ψnew=[ΨsN(s)]-1{ΨsS(s)-diag{ΨsF(s)E[yT(s)]VT}}。
In conclusion the present invention proposes a kind of processing method of voice data, realizes intelligent POS machine terminal identity and recognize Card data are locally stored, compare and operation, need not configure hardware encryption equipment, without payment platform is uploaded to, more pacify Quan Xing.
Obviously, it should be appreciated by those skilled in the art, each module of the above invention or each steps can be with general Computing system realize that they can be concentrated in single computing system, or be distributed in multiple computing systems and formed Network on, optionally, they can be realized with the program code that computing system can perform, it is thus possible to they are stored It is executed within the storage system by computing system.In this way, the present invention is not limited to any specific hardware and softwares to combine.
It should be understood that the above-mentioned specific implementation mode of the present invention is used only for exemplary illustration or explains the present invention's Principle, but not to limit the present invention.Therefore, that is done without departing from the spirit and scope of the present invention is any Modification, equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.In addition, appended claims purport of the present invention Covering the whole variations fallen into attached claim scope and boundary or this range and the equivalent form on boundary and is repairing Change example.

Claims (4)

1. a kind of processing method of voice data, which is characterized in that including：
Intelligent POS machine and payment platform are established by safe lane and connected；
Intelligent POS machine client carries out speech recognition to user；
Subscriber authentication is carried out based on voice recognition result.
2. according to the method described in claim 1, it is characterized in that, the intelligence POS machine client carries out voice knowledge to user Not, further comprise：
Intelligent POS machine obtains the speech recognition request pre-actuated from payment platform；
The identification method currently supported based on intelligent POS machine judges whether the intelligent POS machine supports the speech recognition.
3. according to the method described in claim 1, it is characterized in that, further including：
If judging result is to support speech recognition, intelligent POS machine client carries out user using the recognition result of user speech Authentication；
Alternatively,
According to the authentication mode that the certification request of startup and current intelligent POS machine are supported, it is available to filter out current intelligent POS machine Certification is presented to user, selects and verifies for user.
4. according to the method described in claim 1, it is characterized in that, further including：
User in the RSA key pair generated in security context using the authentication module of intelligence POS machine when starting speech recognition Random number is encrypted in private key, and secret value is returned to payment platform；
Payment platform verifies the validity of secret value using the client public key stored after speech recognition is started；
After intelligent POS machine authentication module completes authentication, in the next use when intelligent POS machine, trusted storage block is directly invoked Abstract is encrypted in the private key of the RSA key centering of middle storage, and secret value is transmitted to payment platform and is verified.
CN201810225485.2A 2018-03-19 2018-03-19 Voice data processing method Active CN108550368B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201810225485.2A CN108550368B (en) 2018-03-19 2018-03-19 Voice data processing method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201810225485.2A CN108550368B (en) 2018-03-19 2018-03-19 Voice data processing method

Publications (2)

Publication Number Publication Date
CN108550368A true CN108550368A (en) 2018-09-18
CN108550368B CN108550368B (en) 2022-05-31

Family

ID=63516562

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201810225485.2A Active CN108550368B (en) 2018-03-19 2018-03-19 Voice data processing method

Country Status (1)

Country Link
CN (1) CN108550368B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111369986A (en) * 2018-12-26 2020-07-03 成都启英泰伦科技有限公司 Intelligent safe voice transmission system and method
CN113495715A (en) * 2020-04-08 2021-10-12 北京意锐新创科技有限公司 Voice issuing method and device suitable for payment equipment management and control platform

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2008038161A2 (en) * 2006-06-12 2008-04-03 Cidway Technologies, Ltd. Secure and portable payment system
CN102057386A (en) * 2008-06-06 2011-05-11 电子湾有限公司 Trusted service manager (TSM) architectures and methods
CN104392353A (en) * 2014-10-08 2015-03-04 无锡指网生物识别科技有限公司 Payment method and system of voice recognition terminal
CN104700261A (en) * 2013-12-10 2015-06-10 中国银联股份有限公司 Security network access initialization method and system for POS terminal

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2008038161A2 (en) * 2006-06-12 2008-04-03 Cidway Technologies, Ltd. Secure and portable payment system
CN102057386A (en) * 2008-06-06 2011-05-11 电子湾有限公司 Trusted service manager (TSM) architectures and methods
CN104700261A (en) * 2013-12-10 2015-06-10 中国银联股份有限公司 Security network access initialization method and system for POS terminal
CN104392353A (en) * 2014-10-08 2015-03-04 无锡指网生物识别科技有限公司 Payment method and system of voice recognition terminal

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
李运蒙: "《信息资源管理》", 31 January 2016, 华南理工大学出版社 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111369986A (en) * 2018-12-26 2020-07-03 成都启英泰伦科技有限公司 Intelligent safe voice transmission system and method
CN113495715A (en) * 2020-04-08 2021-10-12 北京意锐新创科技有限公司 Voice issuing method and device suitable for payment equipment management and control platform

Also Published As

Publication number Publication date
CN108550368B (en) 2022-05-31

Similar Documents

Publication Publication Date Title
US9484037B2 (en) Device, system, and method of liveness detection utilizing voice biometrics
EP2817601B1 (en) System and method for speaker recognition on mobile devices
US20220147602A1 (en) System and methods for implementing private identity
CN110169014A (en) Device, method and computer program product for certification
US20060294390A1 (en) Method and apparatus for sequential authentication using one or more error rates characterizing each security challenge
US20080169903A1 (en) System and Method for Radio Frequency Identifier Voice Signature
US20220147607A1 (en) System and methods for implementing private identity
US20220150068A1 (en) System and methods for implementing private identity
JP2016511475A (en) Method and system for distinguishing humans from machines
JP2003132023A (en) Personal authentication method, personal authentication device and personal authentication system
GB2465782A (en) Biometric identity verification utilising a trained statistical classifier, e.g. a neural network
EP1962280A1 (en) Method and network-based biometric system for biometric authentication of an end user
Ren et al. Secure smart home: A voiceprint and internet based authentication system for remote accessing
US20220277064A1 (en) System and methods for implementing private identity
KR20010009081A (en) Speaker verification system using continuous digits with flexible figures and method thereof
EP4009206A1 (en) System and method for authenticating a user by voice to grant access to data
CN112751838A (en) Identity authentication method, device and system
WO2018191782A1 (en) Voice authentication system and method
US10970573B2 (en) Method and system for free text keystroke biometric authentication
CN109726536A (en) Method for authenticating, electronic equipment and computer-readable program medium
CN108550368A (en) A kind of processing method of voice data
KR101424962B1 (en) Authentication system and method based by voice
CN108416592A (en) A kind of high speed voice recognition methods
EP3373177B1 (en) Methods and systems for determining user liveness
Revathi et al. Person authentication using speech as a biometric against play back attacks

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
TA01 Transfer of patent application right
TA01 Transfer of patent application right

Effective date of registration: 20220509

Address after: 510000 room 3201, No. 2, Huitong Second Street, Hengli Town, Nansha District, Guangzhou City, Guangdong Province

Applicant after: GUANGZHOU HELIPAY PAYMENT TECHNOLOGY Co.,Ltd.

Address before: No.11, 10th floor, building 1, NO.666, Jitai Road, high tech Zone, Chengdu, Sichuan 610000

Applicant before: CHENGDU CINDA OUTWIT TECHNOLOGY CO.,LTD.

GR01 Patent grant
GR01 Patent grant