CN111447327A - Fraud telephone identification method, device, storage medium and terminal - Google Patents
Fraud telephone identification method, device, storage medium and terminal Download PDFInfo
- Publication number
- CN111447327A CN111447327A CN202010183268.9A CN202010183268A CN111447327A CN 111447327 A CN111447327 A CN 111447327A CN 202010183268 A CN202010183268 A CN 202010183268A CN 111447327 A CN111447327 A CN 111447327A
- Authority
- CN
- China
- Prior art keywords
- fraud
- call
- preset
- text data
- terminal
- 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.)
- Pending
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M3/00—Automatic or semi-automatic exchanges
- H04M3/22—Arrangements for supervision, monitoring or testing
- H04M3/2281—Call monitoring, e.g. for law enforcement purposes; Call tracing; Detection or prevention of malicious calls
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; 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
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
- H04W12/12—Detection or prevention of fraud
- H04W12/128—Anti-malware arrangements, e.g. protection against SMS fraud or mobile malware
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/12—Messaging; Mailboxes; Announcements
- H04W4/14—Short messaging services, e.g. short message services [SMS] or unstructured supplementary service data [USSD]
Abstract
The embodiment of the application discloses a method, a device, a storage medium and a terminal for identifying fraudulent calls, wherein the method comprises the following steps: collecting voice data of a current call; converting the voice data into text data according to a voice recognition technology; identifying whether the text data comprises preset keywords or not; if yes, inquiring the fraud cases related to the preset keywords, and displaying the fraud cases through a display unit. According to the embodiment of the application, the phone fraud can be identified through the conversation content, and the risk that a user is deceived by the phone is reduced.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method, an apparatus, a storage medium, and a terminal for identifying a fraud phone.
Background
The activity of telephone fraud is rampant day by day, in order to reduce the property loss of users, in the related art, whether an incoming call is a fraud telephone is identified through a telephone number, a terminal acquires the incoming call number when receiving the incoming call, then inquires whether the incoming call number is marked as a fraud telephone in a database, and if the incoming call number is marked, the terminal displays a prompt message of the suspected fraud telephone of the incoming call on an incoming call interface. However, the fraudulent person can continuously change the telephone number to dial out the fraudulent call, so that the incoming telephone number is not marked in the database, and the risk of the user being defrauded is still high.
Disclosure of Invention
The embodiment of the application provides a method, a device, a storage medium and a terminal for identifying a fraud phone, which can identify the fraud phone according to the content of the current call so as to reduce the risk of fraud of a user. The technical scheme is as follows:
in a first aspect, an embodiment of the present application provides a method for identifying a fraudulent phone call, the method including:
collecting voice data of a current call;
converting the voice data into text data according to a voice recognition technology;
identifying whether the text data comprises preset keywords or not;
if yes, inquiring the fraud cases related to the preset keywords, and displaying the fraud cases through a display unit.
In a second aspect, an embodiment of the present application provides an apparatus for identifying a fraudulent phone call, the apparatus including:
the acquisition unit is used for acquiring the voice data of the current call;
a conversion unit for converting the voice data into text data according to a voice recognition technique;
the identification unit is used for identifying whether the text data comprises preset keywords or not;
if yes, inquiring the fraud cases related to the preset keywords, and displaying the fraud cases through a display unit.
In a third aspect, embodiments of the present application provide a computer storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform the above-mentioned method steps.
In a fourth aspect, an embodiment of the present application provides a terminal, which may include: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the above-mentioned method steps.
The beneficial effects brought by the technical scheme provided by some embodiments of the application at least comprise:
the method comprises the steps of collecting voice data of a current call, converting the voice data into text data, inquiring fraud cases related to preset keywords when the text data are identified to comprise the preset keywords, and displaying the fraud cases through a display unit. Therefore, the fraud telephone can be identified through the content of the current call, the fraud case is displayed, and the user can recognize the geiger of the fraud molecule by reading the reminding of the fraud case, so that the risk of being cheated is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a fraud telephone identification method provided by an embodiment of the present application;
FIG. 2 is a schematic diagram of a user interface provided by an embodiment of the present application;
FIG. 3 is an exemplary diagram of a user interface provided by an embodiment of the present application;
FIG. 4 is an exemplary diagram of a user interface provided by an embodiment of the present application;
FIG. 5 is an exemplary diagram of a user interface provided by an embodiment of the present application;
FIG. 6 is a flow chart of a fraud telephone identification method provided by the embodiment of the present application;
FIG. 7 is a schematic structural diagram of an identification device of a fraudulent telephone provided by an embodiment of the present application;
FIG. 8 is a schematic structural diagram of an identification device of a fraudulent telephone provided by an embodiment of the present application;
fig. 9 is a schematic structural diagram of a terminal according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the application, as detailed in the appended claims.
In the description of the present application, it is to be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The specific meaning of the above terms in the present application can be understood in a specific case by those of ordinary skill in the art. Further, in the description of the present application, "a plurality" means two or more unless otherwise specified. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.
The identification method of fraudulent calls provided by the embodiment of the present application will be described in detail below with reference to fig. 1 to 6. The method may be implemented by means of a computer program, which may be run on an identification means of fraudulent telephones based on the von neumann architecture. The computer program may be integrated into the application or may run as a separate tool-like application. The identification device of the fraud phone in the embodiment of the present application may be a terminal, including but not limited to a smartphone, a personal computer, a tablet computer, a handheld device, a vehicle-mounted device, a wearable device, a computing device, or other processing device connected to a wireless modem.
Please refer to fig. 1, which is a flowchart illustrating a method for identifying fraudulent calls according to an embodiment of the present application. As shown in fig. 1, the method of the embodiment of the present application may include the steps of:
s101, voice data of the current call are collected.
When receiving an incoming call request, the terminal displays an incoming call interface, where the incoming call interface includes information such as an incoming call number, a home location, and an operator, for example: referring to fig. 2, the incoming call interface includes the incoming call number: 15888888888, Zhejiang attribution and mobile operator. The incoming call interface also comprises an answering button and a rejecting button, and when a user clicks the answering button, the terminal answers the incoming call request; and when the user clicks the rejection button, the terminal rejects the incoming call request. When a user answers a call request, the terminal displays a call interface, and the terminal acquires the voice data of the current call through a voice acquisition unit (such as a microphone), wherein the call interface can also comprise the following information besides the information in the call interface: call duration, record button, end call button, memo button, contact button, and the like.
For example: referring to the call interface shown in fig. 3, the call duration displayed by the terminal is 34 seconds, and the terminal further displays that the current call is in a recording state.
The terminal can preset a setting interface for closing or opening the fraud protection function, and when the user opens the fraud protection function through the setting interface, the terminal can automatically record the current call when the user answers the current call, and store voice data generated by recording in a local memory; when a user closes the fraud protection function through the setting interface, the terminal cannot automatically record the current call, the terminal is provided with a recording button on the call interface, and when the user records the current call through the recording button, the voice data generated by recording is stored in a local memory.
And S102, converting the voice data into text data according to a voice recognition technology.
The construction process of the voice recognition system integrally comprises two parts: training and identifying. Training is usually completed off-line, signal processing and knowledge mining are carried out on mass voice data and a language database which are collected in advance, and an acoustic model and a language model which are required by a voice recognition system are obtained; the recognition process is usually completed on line, and the real-time voice data of the user is automatically recognized. The recognition process can be generally divided into two major modules, namely a front-end module and a back-end module: the front-end module is mainly used for carrying out end point detection (removing redundant mute and non-speaking sound), noise reduction, feature extraction and the like; the 'back-end' module is used for carrying out statistical pattern recognition (also called 'decoding') on the feature vector of the user speaking by utilizing the trained 'acoustic model' and 'language model' to obtain text data contained in the voice data, and in addition, the back-end module also has a 'self-adaptive' feedback module which can carry out self-learning on the voice of the user, thereby carrying out necessary 'correction' on the 'acoustic model' and the 'voice model' and further improving the recognition accuracy.
S103, if the text data is identified to comprise the preset keywords, inquiring fraud cases of the preset keywords, and displaying the fraud cases through a display unit.
The terminal may extract preset keywords from a plurality of fraud cases in advance, the algorithm for extracting the preset keywords may be a TF-IDF (term frequency-inverse document frequency) algorithm, and the number of the preset keywords may be one or more. When the terminal identifies that the text data in the S102 includes the preset keyword, a fraud case associated with the preset keyword can be displayed on the call interface through the display unit, and the user can recognize the geiger of the fraud molecule by reading the fraud case, thereby reducing the risk of being cheated.
For example: referring to fig. 4 and 5, when the preset keywords are "money laundering", "public security organization" and "supervision account", and the terminal recognizes that the text data includes the preset keywords, the terminal displays prompt messages of "suspected fraud phone" and "account money laundering fraud" on the call interface, the account money laundering fraud may be associated with a hyperlink address, and the user clicks the hyperlink address to display a fraud case as shown in fig. 5.
In the embodiment of the application, the voice data of the current call is collected, the voice data is converted into the text data, when the text data is identified to comprise the preset keywords, the fraud case associated with the preset keywords is inquired, and the fraud case is displayed through the display unit. Therefore, the fraud telephone can be identified through the content of the current call, the fraud case is displayed, and the user can recognize the geiger of the fraud molecule by reading the reminding of the fraud case, so that the risk of being cheated is reduced.
Please refer to fig. 6, which is a flowchart illustrating a fraud phone identification method according to an embodiment of the present application. The identification method of the fraud phone can comprise the following steps:
s201, collecting voice data of the current call.
When a user answers a call request of the terminal, the terminal collects voice data of the current call according to the audio collection unit and stores the collected voice data in a local memory. Optionally, in order to protect the privacy of the user, the terminal may only temporarily store the voice data of the current call, and after the current call is ended, the terminal may delete the voice data of the current call.
S202, extracting acoustic characteristic parameters of the voice data.
The acoustic feature parameter may be Mel-frequency cepstrum coefficients MFCC, which take human auditory features into consideration, and first map a linear spectrum into a Mel nonlinear spectrum based on auditory perception, and then convert the Mel nonlinear spectrum onto the cepstrum. The Mel Spectrum is obtained by passing the Spectrum through a set of Mel filters, the formula is expressed as log X [ k ] ═ log (Mel-Spectrum), at this time, cepstrum analysis is carried out on the log X [ k ], and cepstrum coefficients h [ k ] obtained on the Mel Spectrum are called Mel frequency cepstrum coefficients, which are called MFCC for short. Usually, before calculating the MFCC, the MFCC analyzes the spectrum signal of the spectral spectrogram of the original sound signal by means of pre-emphasis, framing and windowing, short-time FFT, and the like.
The process of extracting MFCC features includes:
1) pre-emphasis, framing and windowing are performed on voice;
2) for each short-time analysis window, obtaining a corresponding frequency spectrum through FFT;
3) the spectrum above is processed by a Mel filter bank to obtain a Mel spectrum;
4) performing cepstrum analysis on the Mel frequency spectrum (taking logarithm, performing inverse transformation, wherein the actual inverse transformation generally replaces the IDFT by DCT discrete cosine transformation, and taking the 2 nd to 13 th coefficients after DCT as MFCC coefficients) to obtain Mel frequency cepstrum coefficients MFCC.
S203, obtaining text data through an acoustic model and a language model based on the acoustic characteristic parameters.
The construction process of the voice recognition system integrally comprises two parts: training and identifying. Training is usually completed off-line, signal processing and knowledge mining are carried out on mass voice data and a language database which are collected in advance, and an acoustic model and a language model which are required by a voice recognition system are obtained; the recognition process is usually completed on line, and the real-time voice data of the user is automatically recognized. The recognition process can be generally divided into two major modules, namely a front-end module and a back-end module: the front-end module is mainly used for carrying out end point detection (removing redundant mute and non-speaking sound), noise reduction, feature extraction and the like; the 'back-end' module is used for carrying out statistical mode recognition (also called 'decoding') on acoustic characteristic parameters (characteristic vectors) of user speaking by utilizing a trained 'acoustic model' and a 'language model' to obtain text data contained in voice data, and in addition, the back-end module is also provided with a 'self-adaptive' feedback module which can carry out self-learning on the voice of a user, thereby carrying out necessary 'correction' on the 'acoustic model' and the 'language model' and further improving the recognition accuracy.
And S204, identifying whether the text data comprises preset keywords or not.
The terminal may pre-store or preset keywords, the number of the preset keywords may be one or more, and the terminal identifies whether the text data in S203 includes the preset keywords.
And S205, when the identification result is yes, reminding the user that the current call is a fraud call through a preset reminding mode.
Wherein, the user may not notice the message displayed on the screen during the call, so when the terminal includes the keyword in the text data, through a preset reminding manner, for example: and reminding the user that the current call is a fraud call in a vibration mode or a mode of playing the current call through a loudspeaker and the like, wherein the user can notice the content displayed on the display screen of the terminal at the moment.
S206, inquiring fraud cases associated with the preset keywords on a server deployed in the Internet.
The server of the internet stores fraud cases related to preset keywords, when the recognition result of the terminal in S204 is yes, the server is queried about the preset keywords in the analysis in the text data, and the server returns the fraud cases queried based on the preset keywords to the terminal.
And S207, sending a fraud prompting message to a terminal corresponding to the telephone number pre-stored by the user.
When the identification result of S204 is yes, the terminal sends a fraud prompting message to the terminal corresponding to the telephone number to notify the current calling incoming number, calling time, preset keywords and fraud cases, and notifies others of the possibility that the user is subjected to fraud, and the others can notify the user of the possibility of fraudulent calling, so as to reduce the possibility that the user is swindled. The fraud notification message may be a short message, a mail, an instant messaging message, or the like, and the application is not limited thereto.
And S208, displaying the fraud case through a display unit.
The terminal can display the fraud case on the call interface of the current call and can also display the fraud case on other interfaces, wherein when the fraud case is displayed on the call interface, the terminal can use a window of the full-screen fraud case, a closing button is arranged on the window, and after the terminal detects the clicking operation of the closing button by the user, the window can be closed and the fraud case is not displayed any more, so that the content of the fraud case can be remarkably reminded for the user.
S209, after the current conversation is finished, receiving the fraud prevention reminding short message.
After the current call is finished, the terminal can send a fraud reminding message to the fraud center, the fraud reminding message carries information such as the incoming call number, the call time, the preset keyword and the fraud case, and after the fraud reminding message is received by the fraud reminding center, the fraud reminding message or the fraud reminding incoming call is sent to the terminal, so that the credibility of the user believing to be cheated at the moment is increased.
In the embodiment of the application, the voice data of the current call is collected, the voice data is converted into the text data, when the text data is identified to comprise the preset keywords, the fraud case associated with the preset keywords is inquired, and the fraud case is displayed through the display unit. Therefore, the fraud telephone can be identified through the content of the current call, the fraud case is displayed, and the user can recognize the geiger of the fraud molecule by reading the reminding of the fraud case, so that the risk of being cheated is reduced.
The following are embodiments of the apparatus of the present application that may be used to perform embodiments of the method of the present application. For details which are not disclosed in the embodiments of the apparatus of the present application, reference is made to the embodiments of the method of the present application.
Please refer to fig. 7, which shows a schematic structural diagram of an identification apparatus of a fraud phone provided in an exemplary embodiment of the present application. The identification means of the fraudulent phone may be implemented as all or part of the terminal by software, hardware or a combination of both. The device 1 comprises an acquisition unit 10, a conversion unit 20 and an identification unit 30.
The acquisition unit 10 is used for acquiring voice data of the current call;
a conversion unit 20 for converting the voice data into text data according to a voice recognition technique;
an identifying unit 30 configured to identify whether the text data includes a preset keyword;
if yes, inquiring the fraud cases related to the preset keywords, and displaying the fraud cases through a display unit.
In one or more embodiments, the converting the speech data to text data according to a speech recognition technique includes:
extracting acoustic characteristic parameters of the voice data;
and obtaining the text data through an acoustic model and a language model based on the acoustic characteristic parameters.
In one or more embodiments, the querying for fraud cases associated with the preset keyword includes:
querying a fraud case associated with the preset keyword in a local database; or
Querying a server deployed in the Internet for fraud cases associated with the preset keywords.
In one or more embodiments, said displaying said fraud case by a display unit comprises:
and displaying the fraud case on the display interface of the current call through a display unit.
In one or more embodiments, referring to fig. 8, the apparatus 1 further comprises:
the reminding unit 40 is used for reminding the user that the current call is a fraud call in a preset reminding mode; and the preset reminding mode comprises a vibration reminding mode.
In one or more embodiments, referring to fig. 8, the apparatus 1 further comprises:
a transceiving unit 50 for transmitting a fraud prompting message to a terminal corresponding to a phone number pre-stored by the user; the fraud prompt message comprises an incoming call number, call time, the preset keyword and the fraud case.
In one or more embodiments, referring to fig. 8, the transceiver unit 50 is further configured to:
and after the current conversation is finished, receiving the anti-fraud reminding short message.
It should be noted that, when the identification device for a fraudulent phone provided by the above embodiment executes the identification method for a fraudulent phone, only the division of the above functional modules is taken as an example, in practical applications, the above function distribution can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules, so as to complete all or part of the above described functions. In addition, the device for recovering user data provided by the above embodiment and the embodiment of the identification method for fraudulent calls belong to the same concept, and the detailed implementation process is shown in the embodiment of the method, which is not described herein again.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
In the embodiment of the application, the voice data of the current call is collected, the voice data is converted into the text data, when the text data is identified to comprise the preset keywords, the fraud case associated with the preset keywords is inquired, and the fraud case is displayed through the display unit. Therefore, the fraud telephone can be identified through the content of the current call, the fraud case is displayed, and the user can recognize the geiger of the fraud molecule by reading the reminding of the fraud case, so that the risk of being cheated is reduced.
An embodiment of the present application further provides a computer storage medium, where the computer storage medium may store a plurality of instructions, where the instructions are suitable for being loaded by a processor and executing the method steps in the embodiments shown in fig. 1 to 6, and a specific execution process may refer to specific descriptions of the embodiments shown in fig. 1 to 6, which are not described herein again.
Please refer to fig. 9, which provides a schematic structural diagram of a terminal according to an embodiment of the present application. As shown in fig. 9, the terminal 1000 can include: at least one processor 1001, at least one network interface 1004, a user interface 1003, memory 1005, at least one communication bus 1002.
Wherein a communication bus 1002 is used to enable connective communication between these components.
The user interface 1003 may include a Display screen (Display) and a Camera (Camera), and the optional user interface 1003 may also include a standard wired interface and a wireless interface.
The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
The Memory 1005 may include a Random Access Memory (RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable medium. The memory 1005 may be used to store an instruction, a program, code, a set of codes, or a set of instructions. The memory 1005 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described above, and the like; the storage data area may store data and the like referred to in the above respective method embodiments. The memory 1005 may optionally be at least one memory device located remotely from the processor 1001. As shown in fig. 9, a memory 1005, which is one type of computer storage medium, may include an operating system, a network communication module, a user interface module, and a fraud phone identification application.
In the terminal 1000 shown in fig. 9, the user interface 1003 is mainly used as an interface for providing input for a user, and acquiring data input by the user; and processor 1001 may be used to invoke the identification application of the fraudulent phone stored in memory 1005 and perform in particular the following operations:
collecting voice data of a current call;
converting the voice data into text data according to a voice recognition technology;
identifying whether the text data comprises preset keywords or not;
if yes, inquiring the fraud cases related to the preset keywords, and displaying the fraud cases through a display unit.
In one or more embodiments, processor 1001 performs the converting the voice data into text data according to a voice recognition technique, including:
extracting acoustic characteristic parameters of the voice data;
and obtaining the text data through an acoustic model and a language model based on the acoustic characteristic parameters.
In one or more embodiments, the processor 1001 executes the querying for the fraud case associated with the preset keyword, including:
querying a fraud case associated with the preset keyword in a local database; or
Querying a server deployed in the Internet for fraud cases associated with the preset keywords.
In one or more embodiments, the processor 1001 executes the displaying of the fraud case through the display unit, including:
and displaying the fraud case on the display interface of the current call through a display unit.
In one or more embodiments, processor 1001 is further configured to perform:
reminding a user that the current call is a fraud call in a preset reminding mode; and the preset reminding mode comprises a vibration reminding mode.
In one or more embodiments, processor 1001 is further configured to perform:
sending fraud prompting messages to terminals corresponding to telephone numbers pre-stored by the user; the fraud prompt message comprises an incoming call number, call time, the preset keyword and the fraud case.
In one or more embodiments, processor 1001 is further configured to perform:
and after the current conversation is finished, receiving the anti-fraud reminding short message.
In the embodiment of the application, the voice data of the current call is collected, the voice data is converted into the text data, when the text data is identified to comprise the preset keywords, the fraud case associated with the preset keywords is inquired, and the fraud case is displayed through the display unit. Therefore, the fraud telephone can be identified through the content of the current call, the fraud case is displayed, and the user can recognize the geiger of the fraud molecule by reading the reminding of the fraud case, so that the risk of being cheated is reduced.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a read-only memory or a random access memory.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present application and is not to be construed as limiting the scope of the present application, so that the present application is not limited thereto, and all equivalent variations and modifications can be made to the present application.
Claims (10)
1. A method of identifying fraudulent calls, said method comprising:
collecting voice data of a current call;
converting the voice data into text data according to a voice recognition technology;
identifying whether the text data comprises preset keywords or not;
if yes, inquiring the fraud cases related to the preset keywords, and displaying the fraud cases through a display unit.
2. The method of claim 1, wherein the converting the speech data to text data according to a speech recognition technique comprises:
extracting acoustic characteristic parameters of the voice data;
and obtaining the text data through an acoustic model and a language model based on the acoustic characteristic parameters.
3. The method as claimed in claim 1, wherein said querying for fraud cases associated with said preset keyword comprises:
querying a fraud case associated with the preset keyword in a local database; or
Querying a server deployed in the Internet for fraud cases associated with the preset keywords.
4. The method as recited in claim 1, wherein said displaying said fraud case through a display unit comprises:
and displaying the fraud case on the display interface of the current call through a display unit.
5. The method as claimed in claim 4, wherein said displaying the fraud case on the display interface of the current call through a display unit further comprises:
reminding a user that the current call is a fraud call in a preset reminding mode; and the preset reminding mode comprises a vibration reminding mode.
6. The method of claim 1, further comprising:
sending fraud prompting messages to terminals corresponding to telephone numbers pre-stored by the user; the fraud prompt message comprises an incoming call number, call time, the preset keyword and the fraud case.
7. The method of claim 1, further comprising:
and after the current conversation is finished, receiving the anti-fraud reminding short message.
8. An apparatus for identifying fraudulent calls, said apparatus comprising:
the acquisition unit is used for acquiring the voice data of the current call;
a conversion unit for converting the voice data into text data according to a voice recognition technique;
the identification unit is used for identifying whether the text data comprises preset keywords or not;
if yes, inquiring the fraud cases related to the preset keywords, and displaying the fraud cases through a display unit.
9. A computer storage medium, characterized in that it stores a plurality of instructions adapted to be loaded by a processor and to carry out the method steps according to any one of claims 1 to 7.
10. A terminal, comprising: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the method steps of any of claims 1 to 7.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010183268.9A CN111447327A (en) | 2020-03-16 | 2020-03-16 | Fraud telephone identification method, device, storage medium and terminal |
| PCT/CN2020/134094 WO2021184837A1 (en) | 2020-03-16 | 2020-12-04 | Fraudulent call identification method and device, storage medium, and terminal |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010183268.9A CN111447327A (en) | 2020-03-16 | 2020-03-16 | Fraud telephone identification method, device, storage medium and terminal |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111447327A true CN111447327A (en) | 2020-07-24 |
Family
ID=71654099
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010183268.9A Pending CN111447327A (en) | 2020-03-16 | 2020-03-16 | Fraud telephone identification method, device, storage medium and terminal |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN111447327A (en) |
| WO (1) | WO2021184837A1 (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113257250A (en) * | 2021-05-11 | 2021-08-13 | 歌尔股份有限公司 | Fraud behavior detection method, device and storage medium |
| WO2021184837A1 (en) * | 2020-03-16 | 2021-09-23 | 宇龙计算机通信科技(深圳)有限公司 | Fraudulent call identification method and device, storage medium, and terminal |
| CN114598777A (en) * | 2022-02-25 | 2022-06-07 | 马上消费金融股份有限公司 | Intention detection method, device, electronic equipment and storage medium |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115022464A (en) * | 2022-05-06 | 2022-09-06 | 中国联合网络通信集团有限公司 | Number processing method, system, computing device and storage medium |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103685613A (en) * | 2013-12-05 | 2014-03-26 | 江苏大学 | Telephone-fraud-resistant system based on voice recognition and method thereof |
| CN104427079A (en) * | 2013-09-09 | 2015-03-18 | 中兴通讯股份有限公司 | Early warning method and device of user voice calls |
| JP2015220594A (en) * | 2014-05-16 | 2015-12-07 | 日本電信電話株式会社 | Specific speech notification device and specific speech notification method |
| CN106412348A (en) * | 2016-11-14 | 2017-02-15 | 商客通尚景科技（上海）股份有限公司 | Tele-recording recognition early warning control method |
| CN107360576A (en) * | 2017-07-25 | 2017-11-17 | 上海思依暄机器人科技股份有限公司 | A kind of method and system for identifying fraud information, electronic equipment, server |
| CN108134998A (en) * | 2017-12-05 | 2018-06-08 | 南京南邮信息产业技术研究院有限公司 | Information fraud method for early warning and system based on mobile big data |
| CN108616890A (en) * | 2018-04-16 | 2018-10-02 | 中山易美信息技术有限公司 | A kind of swindle ticket analysis system |
| CN109547651A (en) * | 2018-10-22 | 2019-03-29 | 北京奇虎科技有限公司 | A kind of reminding method, device and computer storage medium |
| CN110312031A (en) * | 2018-12-27 | 2019-10-08 | 华为终端有限公司 | Incoming number processing method and electronic equipment |
| CN110381219A (en) * | 2019-06-18 | 2019-10-25 | 武汉华夏理工学院 | A kind of anti-swindle system of communication |
| US20190373105A1 (en) * | 2018-05-30 | 2019-12-05 | International Business Machines Corporation | Cognitive telephone fraud detection |
| CN110738562A (en) * | 2019-10-16 | 2020-01-31 | 支付宝(杭州)信息技术有限公司 | Method, device and equipment for generating risk reminding information |
| CN110868501A (en) * | 2019-11-13 | 2020-03-06 | 刘峰刚 | Fraud prevention method based on voice recognition and fraud prevention hearing aid |
| JP2020038593A (en) * | 2018-09-06 | 2020-03-12 | 桑原 徹 | Telephone fraud reporting device |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111447327A (en) * | 2020-03-16 | 2020-07-24 | 宇龙计算机通信科技(深圳)有限公司 | Fraud telephone identification method, device, storage medium and terminal |
-
2020
- 2020-03-16 CN CN202010183268.9A patent/CN111447327A/en active Pending
- 2020-12-04 WO PCT/CN2020/134094 patent/WO2021184837A1/en active Application Filing
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104427079A (en) * | 2013-09-09 | 2015-03-18 | 中兴通讯股份有限公司 | Early warning method and device of user voice calls |
| CN103685613A (en) * | 2013-12-05 | 2014-03-26 | 江苏大学 | Telephone-fraud-resistant system based on voice recognition and method thereof |
| JP2015220594A (en) * | 2014-05-16 | 2015-12-07 | 日本電信電話株式会社 | Specific speech notification device and specific speech notification method |
| CN106412348A (en) * | 2016-11-14 | 2017-02-15 | 商客通尚景科技（上海）股份有限公司 | Tele-recording recognition early warning control method |
| CN107360576A (en) * | 2017-07-25 | 2017-11-17 | 上海思依暄机器人科技股份有限公司 | A kind of method and system for identifying fraud information, electronic equipment, server |
| CN108134998A (en) * | 2017-12-05 | 2018-06-08 | 南京南邮信息产业技术研究院有限公司 | Information fraud method for early warning and system based on mobile big data |
| CN108616890A (en) * | 2018-04-16 | 2018-10-02 | 中山易美信息技术有限公司 | A kind of swindle ticket analysis system |
| US20190373105A1 (en) * | 2018-05-30 | 2019-12-05 | International Business Machines Corporation | Cognitive telephone fraud detection |
| JP2020038593A (en) * | 2018-09-06 | 2020-03-12 | 桑原 徹 | Telephone fraud reporting device |
| CN109547651A (en) * | 2018-10-22 | 2019-03-29 | 北京奇虎科技有限公司 | A kind of reminding method, device and computer storage medium |
| CN110312031A (en) * | 2018-12-27 | 2019-10-08 | 华为终端有限公司 | Incoming number processing method and electronic equipment |
| CN110381219A (en) * | 2019-06-18 | 2019-10-25 | 武汉华夏理工学院 | A kind of anti-swindle system of communication |
| CN110738562A (en) * | 2019-10-16 | 2020-01-31 | 支付宝(杭州)信息技术有限公司 | Method, device and equipment for generating risk reminding information |
| CN110868501A (en) * | 2019-11-13 | 2020-03-06 | 刘峰刚 | Fraud prevention method based on voice recognition and fraud prevention hearing aid |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021184837A1 (en) * | 2020-03-16 | 2021-09-23 | 宇龙计算机通信科技(深圳)有限公司 | Fraudulent call identification method and device, storage medium, and terminal |
| CN113257250A (en) * | 2021-05-11 | 2021-08-13 | 歌尔股份有限公司 | Fraud behavior detection method, device and storage medium |
| CN114598777A (en) * | 2022-02-25 | 2022-06-07 | 马上消费金融股份有限公司 | Intention detection method, device, electronic equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2021184837A1 (en) | 2021-09-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111447327A (en) | Fraud telephone identification method, device, storage medium and terminal | |
| CN110392913B (en) | Processing calls on a common voice-enabled device | |
| CN103888581B (en) | A kind of communication terminal and its method for recording call-information | |
| CN105516989A (en) | Method and device for identifying bad conversation | |
| CN104050966A (en) | Voice interaction method of terminal equipment and terminal equipment employing voice interaction method | |
| KR101795593B1 (en) | Device and method for protecting phone counselor | |
| CN108010513B (en) | Voice processing method and device | |
| US20080085742A1 (en) | Mobile communication terminal | |
| WO2014154057A1 (en) | Pre-alarming method and device for user voice call and computer storage medium | |
| EP1170932A1 (en) | Audible identification of caller and callee for mobile communication device | |
| CN111683317B (en) | Prompting method and device applied to earphone, terminal and storage medium | |
| CN101485188A (en) | Method and system for providing voice analysis service, and apparatus therefor | |
| CN103249034A (en) | Method and device for acquiring contact information | |
| CN106302933A (en) | Voice information whose processing method and terminal | |
| CN110062097B (en) | Crank call processing method and device, mobile terminal and storage medium | |
| CN104967719A (en) | Contact information prompting method and terminal | |
| CN109151148B (en) | Call content recording method, device, terminal and computer readable storage medium | |
| CN106911706A (en) | call background adding method and device | |
| CN111343328B (en) | Voice print recognition-based call management method and system and mobile terminal | |
| US20230247131A1 (en) | Presentation of communications | |
| KR101033870B1 (en) | Method and device for processing spam call | |
| CN107154996B (en) | Incoming call interception method and device, storage medium and terminal | |
| US8861699B2 (en) | Electronic display method and device | |
| CN111970295B (en) | Multi-terminal-based call transaction management method and device | |
| CN111601000B (en) | Communication network fraud identification method and device and electronic equipment |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200724 |