CN111723180A - Interviewing method and device - Google Patents
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Abstract
The invention discloses an interview method and device, and relates to the technical field of computers. One embodiment of the method comprises: extracting first keyword information from the resume of the interviewer, and extracting second keyword information from the post requirement; screening a plurality of test questions from an interview question library according to the first keyword information and the second keyword information; collecting interview videos of the interviewer when the interviewer answers the plurality of test questions; calculating an interview result of the interviewer based on the audio and video frames of the interview video. The embodiment can solve the technical problems of inaccurate interview results and low interview efficiency.
Description
Technical Field
The invention relates to the technical field of computers, in particular to an interview method and device.
Background
At present, the interview flow of most enterprises is complex, recruiters need to select qualified resumes from the resumes of tens of thousands of applicants, and then interview and investigate the applicants through a series of interview steps.
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art:
most interviewing processes are cumbersome and time consuming, requiring enterprise recruiters to schedule the interviewer and applicant times and interview sites carefully. The series of complex recruitment processes waste the time of recruiters and related personnel participating in the interview, waste resources of enterprises and cause low interview efficiency.
In addition to the above-mentioned traditional interview process, there are some intelligent interview systems currently, but the interview problems contained in the existing intelligent interview technology are uniform, after the use time is too long, the interviewer can easily find the corresponding question bank on the internet for preparation in advance, and there is no specific question for the actual situation of the interviewer, so that the interview result is not accurate enough.
Disclosure of Invention
In view of this, embodiments of the present invention provide an interview method and apparatus to solve the technical problems of inaccurate interview result and low interview efficiency.
To achieve the above object, according to an aspect of an embodiment of the present invention, there is provided an interview method including:
extracting first keyword information from the resume of the interviewer, and extracting second keyword information from the post requirement;
screening a plurality of test questions from an interview question library according to the first keyword information and/or the second keyword information;
collecting interview videos of the interviewer when the interviewer answers the plurality of test questions;
calculating an interview result of the interviewer based on the audio and video frames of the interview video.
Optionally, calculating an interview result of the interviewer based on the audio and video frames of the interview video comprises:
for each of the number of test questions:
converting the audio in the interview video into text information by using a voice recognition technology, and comparing the text information with the answer text of the interview questions to obtain an initial interview result;
performing credit recognition on audio and video frames of the interview video to obtain a credit parameter;
modifying the initial interview results based on the credit value parameters;
and adding the corrected initial interview results of each test question to obtain the interview result of the interviewer.
Optionally, performing credit recognition on the audio and video frames of the interview video to obtain a credit parameter, comprising:
carrying out voiceprint feature detection on the audio frequency of the interview video by utilizing a voice feature identification technology to obtain a voiceprint credit value parameter;
identifying the micro-expression of the interviewer in the video frame of the interview video by using a micro-expression identification technology to obtain a micro-expression credit value parameter;
and identifying the limb of the interviewer in the video frame of the interview video by utilizing a limb identification technology to obtain a limb credit value parameter.
Optionally, modifying the initial interview result based on the credit parameter comprises:
and respectively taking the voiceprint credit value parameter, the micro-expression credit value parameter and the limb credit value parameter as weights of the initial interview result, and correcting the initial interview result through the weights.
Optionally, screening a plurality of test questions from an interview question library according to the first keyword information and/or the second keyword information, including:
screening a plurality of test questions of which the labels are matched with the first keyword information and/or the second keyword information from an interview question library;
wherein each test in the interview question library is labeled with at least one label.
Optionally, the interview database comprises a plurality of sub-interview databases, and the interview difficulty grades of the sub-interview databases are different;
screening a plurality of test questions from an interview question library according to the first keyword information and the second keyword information, wherein the screening comprises the following steps:
and respectively screening a plurality of test questions with labels matched with the first keyword information and/or the second keyword information from each sub-interview question library.
Optionally, each sub-surface test question bank is respectively configured with a test question weight;
adding the corrected initial interview results of each test question to obtain the interview result of the interviewer, wherein the method comprises the following steps:
and based on the test question weight of each test question in the plurality of test questions, carrying out weighted summation on the corrected initial interview results of each test question so as to obtain the interview results of the interviewer.
In addition, according to another aspect of the embodiments of the present invention, there is provided an interview apparatus including:
the extraction module is used for extracting first keyword information from the resume of the interviewer and extracting second keyword information from the position requirement;
the screening module is used for screening a plurality of test questions from an interview question library according to the first keyword information and/or the second keyword information;
the acquisition module is used for acquiring interview videos when the interviewer answers the plurality of test questions;
and the calculating module is used for calculating the interview result of the interviewer based on the audio and video frames of the interview video.
Optionally, the computing module is further configured to:
for each of the number of test questions:
converting the audio in the interview video into text information by using a voice recognition technology, and comparing the text information with the answer text of the interview questions to obtain an initial interview result;
performing credit recognition on audio and video frames of the interview video to obtain a credit parameter;
modifying the initial interview results based on the credit value parameters;
and adding the corrected initial interview results of each test question to obtain the interview result of the interviewer.
Optionally, the computing module is further configured to:
carrying out voiceprint feature detection on the audio frequency of the interview video by utilizing a voice feature identification technology to obtain a voiceprint credit value parameter;
identifying the micro-expression of the interviewer in the video frame of the interview video by using a micro-expression identification technology to obtain a micro-expression credit value parameter;
and identifying the limb of the interviewer in the video frame of the interview video by utilizing a limb identification technology to obtain a limb credit value parameter.
Optionally, the computing module is further configured to:
and respectively taking the voiceprint credit value parameter, the micro-expression credit value parameter and the limb credit value parameter as weights of the initial interview result, and correcting the initial interview result through the weights.
Optionally, the screening module is further configured to:
screening a plurality of test questions of which the labels are matched with the first keyword information and/or the second keyword information from an interview question library;
wherein each test in the interview question library is labeled with at least one label.
Optionally, the interview database comprises a plurality of sub-interview databases, and the interview difficulty grades of the sub-interview databases are different;
optionally, the screening module is further configured to:
and respectively screening a plurality of test questions with labels matched with the first keyword information and/or the second keyword information from each sub-interview question library.
Optionally, each sub-surface test question bank is respectively configured with a test question weight;
the calculation module is further to:
and based on the test question weight of each test question in the plurality of test questions, carrying out weighted summation on the corrected initial interview results of each test question so as to obtain the interview results of the interviewer.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including:
one or more processors;
a storage device for storing one or more programs,
when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any of the embodiments described above.
According to another aspect of the embodiments of the present invention, there is also provided a computer readable medium, on which a computer program is stored, which when executed by a processor implements the method of any of the above embodiments.
One embodiment of the above invention has the following advantages or benefits: because the technical means of collecting the interview videos when the interviewer answers the plurality of interview questions and calculating the interview results of the interviewer based on the audio and video frames of the interview videos is adopted, the technical problems of inaccurate interview results and low interview efficiency in the prior art are solved. According to the embodiment of the invention, the appropriate test questions are screened out through the keyword information in the resume and post requirements of the interviewer, then the analysis and calculation are carried out on the audio and video dimensions, and the initial interview result is corrected according to the calculation result, so that the interview efficiency is improved, and the accuracy of the interview result can be improved.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a schematic diagram of a main flow of an interview method according to an embodiment of the invention;
FIG. 2 is a schematic flow chart of key information extraction and screening of test questions according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart of generating interview results according to an embodiment of the invention;
FIG. 4 is a schematic view of a main flow of an interview method according to a reference embodiment of the present invention;
FIG. 5 is a schematic diagram of the major modules of an interview apparatus according to an embodiment of the invention;
FIG. 6 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
fig. 7 is a schematic block diagram of a computer system suitable for use in implementing a terminal device or server of an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a schematic diagram of the main flow of an interview method according to an embodiment of the invention. As an embodiment of the present invention, as shown in fig. 1, the interview method may include:
step 101, extracting first keyword information from the interviewer resume and extracting second keyword information from the post requirement.
In order to screen out a proper test question, first keyword information in the resume of the interviewer, such as keyword information of work experience, project experience, mastery technology, academic calendar and the like, and second keyword information in the post requirement of the recruitment company, such as keyword information of work experience, project experience, mastery technology, academic calendar and the like, are extracted through a natural language processing technology.
And step 102, screening a plurality of test questions from an interview question library according to the first keyword information and/or the second keyword information.
The examination questions are screened out from the interview question bank according to the first keyword information and/or the second keyword information, the ability of the interviewer and the requirement of the recruitment company can be accurately grasped, and the screening of the appropriate examination questions is facilitated, so that the more real characteristics and the ability of the interviewer can be known.
Optionally, step 102 may comprise: screening a plurality of test questions of which the labels are matched with the first keyword information and/or the second keyword information from an interview question library; wherein each test in the interview question library is labeled with at least one label. In the embodiment of the present invention, it is preferable to screen out a plurality of test questions whose labels are matched with both the first keyword information and the second keyword information, and if the number of the screened test questions is less than a preset threshold, the test questions matched with the first keyword information or the second keyword information are screened out again in consideration until the number of the test questions reaches the preset threshold.
Optionally, the interview database comprises a plurality of sub-interview databases, and the interview difficulty grades of the sub-interview databases are different. The interview system can randomly screen the test questions from a plurality of sub-interview question banks and can also screen the test questions from a plurality of sub-interview question banks according to a preset rule. Because the difficulty of the test questions in each sub-surface test question bank is different, the test questions which are in line with different interviewers can be screened from different sub-surface test question banks aiming at different interviewers. Optionally, each sub-surface test question bank is configured with a test question weight, and in order to accurately calculate the final interview result, the test question weights may be configured for each sub-surface test question bank in advance, and the weights of the test questions in the same sub-surface test question bank are the same.
Optionally, screening a plurality of test questions from an interview question library according to the first keyword information and the second keyword information, including: and respectively screening a plurality of test questions with labels matched with the first keyword information and/or the second keyword information from each sub-interview question library. In order to comprehensively assess the interviewer, a plurality of test questions with labels matched with the first keyword information and/or the second keyword information can be respectively screened from each sub-interview question bank, and simultaneously, the interview result of the interviewer can be accurately calculated by combining the test question weight of each sub-interview question bank.
For example, as shown in FIG. 2, in order to fully understand the more realistic features and abilities of the interviewer, the embodiment of the present invention divides the test questions into several broad categories: conventional questions, company demand questions, resume related questions, and promotion questions.
And then screening a plurality of test questions with labels matched with the first keyword information and/or the second keyword information from the corresponding sub-interview question library respectively. Each type of the test question library occupies different test question weights, and the question library can be updated frequently, so that the cost for obtaining answers is increased, and the probability of screening qualified interviewers can be effectively improved.
And 103, collecting interview videos when the interviewer answers the plurality of test questions.
After the test questions are screened out, the interviewer automatically accesses the remote interview system, the interview system enables the interviewer to answer the test questions screened out in the step 102, and meanwhile videos of the interviewer when the interviewer answers the test questions are collected.
And 104, calculating an interview result of the interviewer based on the audio and video frames of the interview video.
After the interview video of the interviewer is collected, the interview related results are respectively calculated based on the audio and video frames of the interview video, and therefore the final interview result of the interviewer is obtained. The embodiment of the invention analyzes and calculates the interview video from the audio dimension and the video frame dimension respectively, and is beneficial to improving the calculation accuracy of the interview result.
Optionally, step 104 may include: for each of the number of test questions: converting the audio in the interview video into text information by utilizing a voice recognition technology, and comparing the text information with answer texts of the plurality of interview questions to obtain an initial interview result; performing credit recognition on audio and video frames of the interview video to obtain a credit parameter; modifying the initial interview results based on the credit value parameters; and adding the corrected initial interview results of each test question to obtain the interview result of the interviewer. In the embodiment of the invention, the interview video is further analyzed and calculated from the audio and video frame dimensions on the basis of the semantic recognition result for each test question in the plurality of test questions, which is favorable for improving the calculation accuracy of the interview result.
Optionally, performing credit recognition on the audio and video frames of the interview video to obtain a credit parameter, comprising: carrying out voiceprint feature detection on the audio frequency of the interview video by utilizing a voice feature identification technology to obtain a voiceprint credit value parameter; identifying the micro-expression of the interviewer in the video frame of the interview video by using a micro-expression identification technology to obtain a micro-expression credit value parameter; and identifying the limb of the interviewer in the video frame of the interview video by utilizing a limb identification technology to obtain a limb credit value parameter. As shown in fig. 3, the audio in the interview video is converted into text information by using a voice recognition technology, and the text information of each test question is compared with the answer text to obtain an initial interview result. And then, voice print characteristic detection is carried out on the audio frequency of each test question of the interviewer by utilizing voice characteristic identification, for example, whether the interviewer is really familiar with the content mentioned in the resume is analyzed by judging whether the phenomena such as dragging, blocking and the like exist, and therefore, a voice print credit value parameter is obtained. And simultaneously extracting key frames in the video, and identifying the micro expression and limb action information in each key frame by using micro expression identification and limb identification technologies, such as whether to scratch the head, buckle the hands, touch the nose and the like, and judging whether the information answered by the interviewee is real and effective, so as to obtain the micro expression credit value parameters and the limb credit value parameters.
Optionally, modifying the initial interview result based on the credit parameter comprises: and respectively taking the voiceprint credit value parameter, the micro-expression credit value parameter and the limb credit value parameter as weights of the initial interview result, and correcting the initial interview result through the weights. As shown in fig. 3, after obtaining the voiceprint credit value parameter, the microexpression credit value parameter and the limb credit value parameter, the initial interview result is corrected by using the parameters as weights, so as to obtain the real interview result of each test question.
Adding the corrected initial interview results of each test question to obtain the interview result of the interviewer, wherein the method comprises the following steps: and based on the test question weight of each test question in the plurality of test questions, carrying out weighted summation on the corrected initial interview results of each test question so as to obtain the interview results of the interviewer. Because different test questions are configured with different test question weights, after the real interview result of each test question is obtained, the real interview result of each test question is weighted and summed, so that the final interview result of the interviewer is obtained.
According to the various embodiments, the technical means that the interview result of the interviewer is calculated based on the audio and video frames of the interview video by collecting the interview video when the interviewer answers the plurality of test questions in the embodiments of the invention can be seen, and the technical problems of inaccurate interview result and low interview efficiency in the prior art are solved. According to the embodiment of the invention, the appropriate test questions are screened out through the keyword information in the resume and post requirements of the interviewer, then the analysis and calculation are carried out on the audio and video dimensions, and the initial interview result is corrected according to the calculation result, so that the interview efficiency is improved, and the accuracy of the interview result can be improved.
Fig. 4 is a schematic view of a main flow of an interview method according to a reference embodiment of the present invention. As another embodiment of the present invention, as shown in fig. 4, the interview method may include:
step 401, extracting first keyword information from the interviewer resume, and extracting second keyword information from the post requirement.
Firstly, extracting first keyword information in the biographical notes of interviewees, such as keyword information of work experience, project experience, mastery technology, academic notes and the like, and second keyword information in post requirements of recruiters, such as keyword information of work experience, project experience, mastery technology, academic notes and the like, by using a natural language processing technology.
Step 402, screening out a plurality of test questions with labels matched with the first keyword information and/or the second keyword information from each sub-surface test question library respectively.
Preferably, a plurality of test questions with labels matched with the first keyword information and the second keyword information are screened, if the number of the screened test questions is less than a preset threshold value, the test questions matched with the first keyword information or the second keyword information are screened again in consideration until the number of the test questions reaches the preset threshold value.
The interview question library comprises a plurality of sub-interview question libraries, and the interview question difficulty grades of the sub-interview question libraries are different. The interview system can randomly screen the test questions from a plurality of sub-interview question banks and can also screen the test questions from a plurality of sub-interview question banks according to a preset rule. Because the difficulty of the test questions in each sub-surface test question bank is different, the test questions which are in line with different interviewers can be screened from different sub-surface test question banks aiming at different interviewers. Optionally, each sub-surface test question bank is configured with a test question weight, and in order to accurately calculate the final interview result, the test question weights may be configured for each sub-surface test question bank in advance, and the weights of the test questions in the same sub-surface test question bank are the same.
Step 403, converting the audio in the interview video into text information by using a voice recognition technology, and comparing the text information with the answer text of the interview questions to obtain an initial interview result.
And step 404, performing voiceprint feature detection on the audio frequency of the interview video by using a voice feature identification technology to obtain a voiceprint credit value parameter.
Such as whether to speak, hold, etc., to analyze whether the interviewer is really familiar with the content presented in the resume, and thereby obtain the voiceprint credit parameter.
And 405, identifying the micro expression of the interviewer in the video frame of the interview video by using a micro expression identification technology to obtain a micro expression credit value parameter.
And 406, identifying the limbs of the interviewer in the video frames of the interview video by utilizing a limb identification technology to obtain a limb credit value parameter.
And identifying the micro expression and limb action information in each key frame by utilizing micro expression identification and limb identification technologies, such as whether the head is scratched, the hands are buckled, the nose is touched and the like, and judging whether the information answered by the interviewee is real and effective or not, so that a micro expression credit value parameter and a limb credit value parameter are obtained.
For each test question, the steps 403 and 406 are sequentially executed, so as to obtain the voiceprint credit value parameter, the micro-expression credit value parameter and the limb credit value parameter corresponding to each test question.
It should be noted that, the steps 403-406 may be executed in any order or simultaneously, which is not limited in this embodiment of the present invention.
And 407, respectively taking the voiceprint credit value parameter, the micro-expression credit value parameter and the limb credit value parameter as weights of the initial interview result, and correcting the initial interview result through the weights.
And after obtaining the voiceprint credit value parameter, the micro-expression credit value parameter and the limb credit value parameter corresponding to each test question, taking the parameters as weights, and correcting the initial interview result through the weights, thereby obtaining the real interview result of each test question.
And step 408, based on the test question weight of each test question in the plurality of test questions, performing weighted summation on the corrected initial interview results of each test question to obtain the interview results of the interviewer.
Because different test questions are configured with different test question weights, after the real interview result of each test question is obtained, the real interview result of each test question is weighted and summed, so that the final interview result of the interviewer is obtained.
In addition, in one embodiment of the present invention, the contents of the implementation of the interview method are described in detail in the above interview method, and therefore, the repeated contents are not described again.
Fig. 5 is a schematic diagram of the main modules of an interview apparatus according to an embodiment of the invention, and as shown in fig. 5, the interview apparatus 500 includes an extraction module 501, a screening module 502, an acquisition module 503 and a calculation module 504; the extraction module 501 is configured to extract first keyword information from the interviewer resume, and extract second keyword information from the post requirement; the screening module 502 is configured to screen a plurality of test questions from an interview question library according to the first keyword information and/or the second keyword information; the collecting module 503 is configured to collect interview videos of the interviewer when the interviewer answers the plurality of test questions; the calculation module 504 is configured to calculate interview results of the interviewer based on the audio and video frames of the interview video.
Optionally, the calculation module 504 is further configured to:
for each of the number of test questions:
converting the audio in the interview video into text information by using a voice recognition technology, and comparing the text information with the answer text of the interview questions to obtain an initial interview result;
performing credit recognition on audio and video frames of the interview video to obtain a credit parameter;
modifying the initial interview results based on the credit value parameters;
and adding the corrected initial interview results of each test question to obtain the interview result of the interviewer.
Optionally, the calculation module 504 is further configured to:
carrying out voiceprint feature detection on the audio frequency of the interview video by utilizing a voice feature identification technology to obtain a voiceprint credit value parameter;
identifying the micro-expression of the interviewer in the video frame of the interview video by using a micro-expression identification technology to obtain a micro-expression credit value parameter;
and identifying the limb of the interviewer in the video frame of the interview video by utilizing a limb identification technology to obtain a limb credit value parameter.
Optionally, the calculation module 504 is further configured to:
and respectively taking the voiceprint credit value parameter, the micro-expression credit value parameter and the limb credit value parameter as weights of the initial interview result, and correcting the initial interview result through the weights.
Optionally, the screening module 502 is further configured to:
screening a plurality of test questions of which the labels are matched with the first keyword information and/or the second keyword information from an interview question library;
wherein each test in the interview question library is labeled with at least one label.
Optionally, the interview database comprises a plurality of sub-interview databases, and the interview difficulty grades of the sub-interview databases are different;
optionally, the screening module 502 is further configured to:
and respectively screening a plurality of test questions with labels matched with the first keyword information and/or the second keyword information from each sub-interview question library.
Optionally, each sub-surface test question bank is respectively configured with a test question weight;
the calculation module 504 is further configured to:
and based on the test question weight of each test question in the plurality of test questions, carrying out weighted summation on the corrected initial interview results of each test question so as to obtain the interview results of the interviewer.
According to the various embodiments, the technical means that the interview result of the interviewer is calculated based on the audio and video frames of the interview video by collecting the interview video when the interviewer answers the plurality of test questions in the embodiments of the invention can be seen, and the technical problems of inaccurate interview result and low interview efficiency in the prior art are solved. According to the embodiment of the invention, the appropriate test questions are screened out through the keyword information in the resume and post requirements of the interviewer, then the analysis and calculation are carried out on the audio and video dimensions, and the initial interview result is corrected according to the calculation result, so that the interview efficiency is improved, and the accuracy of the interview result can be improved.
It should be noted that, in the embodiment of the interview apparatus of the present invention, the details of the interview method are described in detail, and therefore, the repeated description is omitted.
Fig. 6 illustrates an exemplary system architecture 600 of an interview method or interview apparatus to which embodiments of the invention may be applied.
As shown in fig. 6, the system architecture 600 may include terminal devices 601, 602, 603, a network 604, and a server 605. The network 604 serves to provide a medium for communication links between the terminal devices 601, 602, 603 and the server 605. Network 604 may include various types of connections, such as wire, wireless communication links, or fiber optic cables, to name a few.
A user may use the terminal devices 601, 602, 603 to interact with the server 605 via the network 604 to receive or send messages or the like. The terminal devices 601, 602, 603 may have installed thereon various communication client applications, such as shopping applications, web browser applications, search applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).
The terminal devices 601, 602, 603 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 605 may be a server providing various services, such as a background management server (for example only) providing support for shopping websites browsed by users using the terminal devices 601, 602, 603. The background management server may analyze and otherwise process the received data such as the item information query request, and feed back a processing result (for example, target push information, item information — just an example) to the terminal device.
It should be noted that the interview method provided by the embodiment of the present invention is generally executed by the server 605, and accordingly, the interview apparatus is generally disposed in the server 605. The interview method provided by the embodiment of the invention can also be executed by the terminal equipment 601, 602 and 603, and correspondingly, the interview device can be arranged in the terminal equipment 601, 602 and 603.
It should be understood that the number of terminal devices, networks, and servers in fig. 6 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 7, shown is a block diagram of a computer system 700 suitable for use with a terminal device implementing an embodiment of the present invention. The terminal device shown in fig. 7 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 7, the computer system 700 includes a Central Processing Unit (CPU)701, which can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. In the RAM703, various programs and data necessary for the operation of the system 700 are also stored. The CPU 701, the ROM 702, and the RAM703 are connected to each other via a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.
The following components are connected to the I/O interface 705: an input portion 706 including a keyboard, a mouse, and the like; an output section 707 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 708 including a hard disk and the like; and a communication section 709 including a network interface card such as a LAN card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. A drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that a computer program read out therefrom is mounted into the storage section 708 as necessary.
In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 709, and/or installed from the removable medium 711. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 701.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer programs according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes an extraction module, a screening module, an acquisition module, and a computation module, where the names of the modules do not in some cases constitute a limitation on the modules themselves.
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, implement the method of: extracting first keyword information from the resume of the interviewer, and extracting second keyword information from the post requirement; screening a plurality of test questions from an interview question library according to the first keyword information and/or the second keyword information; collecting interview videos of the interviewer when the interviewer answers the plurality of test questions; calculating an interview result of the interviewer based on the audio and video frames of the interview video.
According to the technical scheme of the embodiment of the invention, because the technical means of collecting the interview videos when the interviewer answers the plurality of test questions and calculating the interview results of the interviewer based on the audio and video frames of the interview videos is adopted, the technical problems of inaccurate interview results and low interview efficiency in the prior art are solved. According to the embodiment of the invention, the appropriate test questions are screened out through the keyword information in the resume and post requirements of the interviewer, then the analysis and calculation are carried out on the audio and video dimensions, and the initial interview result is corrected according to the calculation result, so that the interview efficiency is improved, and the accuracy of the interview result can be improved.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method of interviewing comprising:
extracting first keyword information from the resume of the interviewer, and extracting second keyword information from the post requirement;
screening a plurality of test questions from an interview question library according to the first keyword information and/or the second keyword information;
collecting interview videos of the interviewer when the interviewer answers the plurality of test questions;
calculating an interview result of the interviewer based on the audio and video frames of the interview video.
2. The method of claim 1, wherein calculating the interview results of the interviewer based on the audio and video frames of the interview video comprises:
for each of the number of test questions:
converting the audio in the interview video into text information by using a voice recognition technology, and comparing the text information with the answer text of the interview questions to obtain an initial interview result;
performing credit recognition on audio and video frames of the interview video to obtain a credit parameter;
modifying the initial interview results based on the credit value parameters;
and adding the corrected initial interview results of each test question to obtain the interview result of the interviewer.
3. The method of claim 2, wherein performing credit recognition on audio and video frames of the interview video to obtain a credit parameter comprises:
carrying out voiceprint feature detection on the audio frequency of the interview video by utilizing a voice feature identification technology to obtain a voiceprint credit value parameter;
identifying the micro-expression of the interviewer in the video frame of the interview video by using a micro-expression identification technology to obtain a micro-expression credit value parameter;
and identifying the limb of the interviewer in the video frame of the interview video by utilizing a limb identification technology to obtain a limb credit value parameter.
4. The method of claim 3, wherein modifying the initial interview results based on the credit parameter comprises:
and respectively taking the voiceprint credit value parameter, the micro-expression credit value parameter and the limb credit value parameter as weights of the initial interview result, and correcting the initial interview result through the weights.
5. The method of claim 2, wherein the step of screening a plurality of test questions from an interview question library according to the first keyword information and/or the second keyword information comprises:
screening a plurality of test questions of which the labels are matched with the first keyword information and/or the second keyword information from an interview question library;
wherein each test in the interview question library is labeled with at least one label.
6. The method of claim 5, wherein the interview database comprises a plurality of sub-interview databases, wherein the level of difficulty of the interview questions in each sub-interview database is different;
screening a plurality of test questions from an interview question library according to the first keyword information and the second keyword information, wherein the screening comprises the following steps:
and respectively screening a plurality of test questions with labels matched with the first keyword information and/or the second keyword information from each sub-interview question library.
7. The method of claim 6, wherein each sub-area test library is configured with a test question weight;
adding the corrected initial interview results of each test question to obtain the interview result of the interviewer, wherein the method comprises the following steps:
and based on the test question weight of each test question in the plurality of test questions, carrying out weighted summation on the corrected initial interview results of each test question so as to obtain the interview results of the interviewer.
8. An interview apparatus, comprising:
the extraction module is used for extracting first keyword information from the resume of the interviewer and extracting second keyword information from the position requirement;
the screening module is used for screening a plurality of test questions from an interview question library according to the first keyword information and/or the second keyword information;
the acquisition module is used for acquiring interview videos when the interviewer answers the plurality of test questions;
and the calculating module is used for calculating the interview result of the interviewer based on the audio and video frames of the interview video.
9. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
the one or more programs, when executed by the one or more processors, implement the method of any of claims 1-7.
10. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112434144A (en) * | 2020-11-23 | 2021-03-02 | 京东数字科技控股股份有限公司 | Method, device, electronic equipment and computer readable medium for generating target problem |
| CN113327104A (en) * | 2021-08-04 | 2021-08-31 | 深圳市启程教育科技有限公司 | Talent interview screening system and method based on image processing |
| CN114862106A (en) * | 2022-03-25 | 2022-08-05 | 国家电投集团科学技术研究院有限公司 | Talent capability evaluation method and device, electronic equipment and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109544104A (en) * | 2018-11-01 | 2019-03-29 | 平安科技（深圳）有限公司 | A kind of recruitment data processing method and device |
| CN110135692A (en) * | 2019-04-12 | 2019-08-16 | 平安普惠企业管理有限公司 | Intelligence grading control method, device, computer equipment and storage medium |
| CN110135800A (en) * | 2019-04-23 | 2019-08-16 | 南京葡萄诚信息科技有限公司 | A kind of artificial intelligence video interview method and system |
| CN110210301A (en) * | 2019-04-26 | 2019-09-06 | 平安科技（深圳）有限公司 | Method, apparatus, equipment and storage medium based on micro- expression evaluation interviewee |
-
2020
- 2020-06-08 CN CN202010514087.XA patent/CN111723180A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109544104A (en) * | 2018-11-01 | 2019-03-29 | 平安科技（深圳）有限公司 | A kind of recruitment data processing method and device |
| CN110135692A (en) * | 2019-04-12 | 2019-08-16 | 平安普惠企业管理有限公司 | Intelligence grading control method, device, computer equipment and storage medium |
| CN110135800A (en) * | 2019-04-23 | 2019-08-16 | 南京葡萄诚信息科技有限公司 | A kind of artificial intelligence video interview method and system |
| CN110210301A (en) * | 2019-04-26 | 2019-09-06 | 平安科技（深圳）有限公司 | Method, apparatus, equipment and storage medium based on micro- expression evaluation interviewee |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112434144A (en) * | 2020-11-23 | 2021-03-02 | 京东数字科技控股股份有限公司 | Method, device, electronic equipment and computer readable medium for generating target problem |
| CN113327104A (en) * | 2021-08-04 | 2021-08-31 | 深圳市启程教育科技有限公司 | Talent interview screening system and method based on image processing |
| CN114862106A (en) * | 2022-03-25 | 2022-08-05 | 国家电投集团科学技术研究院有限公司 | Talent capability evaluation method and device, electronic equipment and storage medium |
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