WO2021057146A1 - Voice-based interviewee determination method and device, terminal, and storage medium - Google Patents

Abstract

A voice-based interviewee determination method, a voice-based interviewee determination device (20), a terminal (3), and a storage medium. The voice-based interviewee determination method comprises: obtaining answer voices of a plurality of questions for an interviewee (S11); slicing the answer voice of each question to obtain a plurality of voice segments (S12); calculating the volume characteristic, the speed characteristic, the continuity duration, and the discontinuity duration for each question according to the plurality of voice segments (S13); determining the emotion stability of the interviewee according to the volume characteristic for each question (S14); determining the speed characteristics, the discontinuity durations, and the continuity durations by using a pre-constructed self-confidence degree determination model to determine the self-confidence degree of the interviewee (S15); determining the speed characteristics and the discontinuity durations by using the pre-constructed self-confidence degree determination model to determine the response speed of the interviewee (S16); and outputting an interview result of the interviewee according to the emotion stability, the response speed, and the self-confidence degree (S17). According to the voice-based interviewee determination method, the interviewee can be objectively and comprehensively evaluated, so that an evaluation result is more precise and accurate.

Description

Voice-based interviewer judgment method, device, terminal and storage medium

This application claims the priority of a Chinese patent application filed with the Chinese Patent Office on September 23, 2019, the application number is 201910900813.9, and the invention title is "Voice-based Interviewer Judgment Method, Device, Terminal, and Storage Medium", and its entire content Incorporated in this application by reference.

Technical field

This application relates to the field of speech recognition technology, and in particular to a method, device, terminal and storage medium for determining interviewers based on speech.

Background technique

Recruitment is an indispensable part of every company. Recruitment efficiency is crucial to the company's next development strategy and company cost. However, due to the large number of applicants, there are many resumes that need to be processed, which brings a large amount of work.

In the prior art, it is mainly through face-to-face communication between the interviewer and the interviewer to determine whether the interviewer meets the recruitment requirements, although there is also a way of human-computer interaction to obtain the interviewer's voice and conduct the interview through voice. However, the inventor realized that this way of human-computer interaction is only to analyze the content of the voice to determine whether the interviewer answered the question correctly, and did not conduct an in-depth analysis of the interviewer’s voice, such as analyzing the emotional stability of the interviewer. , Reaction speed and self-confidence. And these characteristics are also crucial for job matching.

Therefore, how to quickly and comprehensively evaluate interviewers by analyzing voice characteristics in an interview is a technical problem that needs to be solved urgently.

Summary of the invention

In view of the above, it is necessary to propose a voice-based interviewer judgment method, device, terminal, and storage medium. Through in-depth analysis and mining of the human-computer interaction voice during the interview, multiple characteristics of the interviewer are determined, and through these The characteristics of the interviewer are objectively and comprehensively evaluated, and the evaluation results are more precise and accurate.

The first aspect of the present application provides a voice-based interviewer judgment method, the method includes:

Obtain the answer voice of the interviewer's multiple questions;

Slice the answer voice of each question to get multiple voice fragments;

Calculate the volume characteristics, speech rate characteristics, duration, and intermittent duration of each question according to the multiple speech fragments;

Determine the emotional stability of the interviewer according to the volume characteristics of each question;

Use a pre-built confidence judgment model to judge the speaking rate feature, the intermittent duration, and the duration to determine the interviewer's confidence;

Use a pre-built confidence judgment model to judge the speech rate characteristics and the length of the interruption, and determine the interviewer's response speed;

The interview result of the interviewer is output according to the emotional stability, reaction speed and self-confidence.

The second aspect of the present application provides a voice-based interviewer determination device, the device includes:

The acquisition module is used to acquire the answer voice of the interviewer’s multiple questions

The slicing module is used to slice the answer voice of each question to obtain multiple voice fragments;

The calculation module is used to calculate the volume characteristic, the speaking rate characteristic, the duration, and the intermittent duration of each question according to the multiple speech fragments;

The first determining module is configured to determine the emotional stability of the interviewer according to the volume characteristics of each question;

The second determination module is configured to use a pre-built confidence determination model to determine the speaking rate feature, the intermittent duration, and the duration, and determine the interviewer's confidence;

The third determining module is configured to use a pre-built confidence level determination model to determine the speaking rate feature and the interruption duration, and determine the interviewer's response speed;

The output module is used to output the interview result of the interviewer according to the emotional stability, reaction speed and confidence.

A third aspect of the present application provides a terminal, the terminal includes a processor, and the processor is configured to implement the following steps when executing computer-readable instructions stored in a memory:

Obtain the answer voice of the interviewer's multiple questions;

Slice the answer voice of each question to get multiple voice fragments;

Calculate the volume characteristics, speech rate characteristics, duration, and intermittent duration of each question according to the multiple speech fragments;

Determine the emotional stability of the interviewer according to the volume characteristics of each question;

Use a pre-built confidence judgment model to judge the speaking rate feature, the intermittent duration, and the duration to determine the interviewer's confidence;

Use a pre-built confidence judgment model to judge the speech rate characteristics and the length of the interruption, and determine the interviewer's response speed;

The interview result of the interviewer is output according to the emotional stability, reaction speed and self-confidence.

A fourth aspect of the present application provides a computer-readable storage medium having computer-readable instructions stored on the computer-readable storage medium, and when the computer-readable instructions are executed by a processor, the following steps are implemented:

Obtain the answer voice of the interviewer's multiple questions;

Slice the answer voice of each question to get multiple voice fragments;

Calculate the volume characteristics, speech rate characteristics, duration, and intermittent duration of each question according to the multiple speech fragments;

Determine the emotional stability of the interviewer according to the volume characteristics of each question;

Use a pre-built confidence judgment model to judge the speaking rate feature, the intermittent duration, and the duration to determine the interviewer's confidence;

Use a pre-built confidence judgment model to judge the speech rate characteristics and the length of the interruption, and determine the interviewer's response speed;

The interview result of the interviewer is output according to the emotional stability, reaction speed and self-confidence.

In summary, the voice-based interviewer determination method, device, terminal, and storage medium described in this application can be applied to fields such as smart government affairs, thereby promoting the construction of smart cities. This application obtains the answer speech of each question of the interviewer, slices the answer speech of each question to obtain multiple speech fragments, and extracts the volume characteristics, speaking rate characteristics, duration, and intermittent length of each of the speech fragments , Determine the emotional stability of the interviewer based on the volume characteristics, and then use the pre-built confidence judgment model and reaction speed judgment model to judge the speech rate characteristics, duration, and intermittent time to determine the interviewer’s confidence and The reaction speed is to output the interview result of the interviewer according to the emotional stability, reaction speed, and self-confidence. This application uses in-depth analysis and mining of the human-computer interaction voice during the interview process to determine multiple characteristics of the interviewer, such as emotional stability, reaction speed, and self-confidence. Through these characteristics, the interviewer can be evaluated objectively and comprehensively. The result is more precise and accurate, which improves the efficiency and quality of the interview judgment.

Description of the drawings

Fig. 1 is a flowchart of a voice-based interviewer judgment method provided by Embodiment 1 of the present application.

Fig. 2 is a structural diagram of a voice-based interviewer judging device provided in the second embodiment of the present application.

FIG. 3 is a schematic structural diagram of a terminal provided in Embodiment 3 of the present application.

detailed description

In order to be able to understand the above objectives, features and advantages of the application more clearly, the application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments of the application and the features in the embodiments can be combined with each other if there is no conflict.

In the following description, many specific details are set forth in order to fully understand the present application, and the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of this application.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field of this application. The terms used in the specification of the application herein are only for the purpose of describing specific embodiments, and are not intended to limit the application.

Example one

Fig. 1 is a flowchart of a voice-based interviewer judgment method provided by Embodiment 1 of the present application.

In this embodiment, the voice-based interviewer determination method can be applied to a terminal. For a terminal that needs voice-based interviewer determination, the voice-based interview provided by the method of this application can be directly integrated on the terminal. The function determined by the user may be run in the terminal in the form of a Software Development Kit (SKD).

As shown in Fig. 1, the voice-based interviewer judgment method specifically includes the following steps. According to different needs, the order of the steps in the flowchart can be changed, and some of the steps can be omitted.

S11: Acquire answer voices for multiple questions of the interviewer.

Set up multiple questions in advance according to the needs of the recruitment position, and obtain the voice of the interviewer and the machine for each question in the interview process through human-computer interaction, and then separate the question voice issued by the machine from the interviewer’s answer voice , And finally screen out the interviewer’s answer voice.

As an optional embodiment, before the obtaining voice answers to multiple questions of the interviewer, the method further includes:

Construct a self-confidence judgment model and a reaction speed judgment model.

Wherein, the process of constructing the confidence level judgment model and the reaction speed judgment model includes:

Obtain multiple sample voices;

Extracting multiple features in the multiple sample voices;

According to the distribution of the multiple features, the first significant feature with a large degree of confidence and the second significant feature with a large degree of response speed are selected from the multiple features, wherein the first significant Features include: speech rate characteristics, duration, and intermittent duration, and the second significant feature includes: speech rate features, intermittent duration;

Determine the multiple confidence levels corresponding to the multiple first salient features and the feature range corresponding to each of the confidence levels, and determine the multiple response speed levels corresponding to the multiple second salient features and each Describe the characteristic range corresponding to the reaction speed grade;

Judge whether the characteristic ranges of different confidence levels and the characteristic ranges of different reaction speed levels are consistent with extreme values;

If the feature ranges of different confidence levels are consistent with extreme values, construct a confidence determination model based on the multiple first salient features, multiple confidence levels, and the feature range corresponding to each of the confidence levels;

If the characteristic ranges of different reaction speed grades meet the extreme consistency, a reaction speed determination model is constructed based on the plurality of second salient characteristics, the plurality of reaction speed grades, and the characteristic range corresponding to each of the reaction speed grades.

Through a large number of experiments: the self-confidence, emotional stability, and reaction speed of the sample speech of each question answered by multiple interviewers are labeled, and then the four relevant features and the corresponding labeling results are used as the learning object to establish a learning model , Found that: from the data distribution of each relevant feature in different degrees of confidence/emotional stability/reaction speed, the data distribution of people with different confidence/emotional stability/reaction speed is obvious and regular, thus The interviewer's confidence, emotional stability, and reaction speed can be quantitatively evaluated through four relevant characteristics of the interviewer: volume characteristics, speaking rate characteristics, duration and intermittent duration.

Through observation, we found the distribution of four different volume characteristics, speaking rate characteristics, duration, and intermittent duration at different confidence levels and different reaction speeds, and determined the characteristic types with different degrees of confidence and different reaction speeds. A feature type with a relatively large degree of discrimination. According to the four relevant features and confidence levels of the sample speech, generate the first box plots of each relevant feature at different confidence levels and the second box plots of each relevant feature at different reaction speed levels, and start from the first The box chart identifies several first notable features that have a greater degree of discrimination in different levels of confidence: speaking rate characteristics, duration, and intermittent duration, and the second box chart determines the degree of discrimination between different levels of reaction speed The relatively large second significant features: the characteristics of speech rate, the length of the interruption. Finally, a self-confidence judgment model is constructed based on the three first salient features of speech rate, duration, and intermittent duration. A response speed judgment model is constructed based on the two second salient features of speech rate and intermittent duration.

Wherein, the first box diagram is generated from the distribution of the eigenvalues of the first salient feature at different confidence levels, and the second box diagram is generated from the distribution of the eigenvalues of the second salient feature at different reaction speed levels. of.

In the embodiment of the present application, when training the salient feature, it is necessary to determine the salient feature corresponding to the salient feature according to the maximum and minimum values corresponding to the salient feature in the box diagrams of different confidence/reaction speed levels. The range of characteristic values in different levels of confidence/reaction speed. After determining the feature value range corresponding to the salient feature at different confidence levels/reaction speed grades, it is necessary to determine whether the feature value range conforms to the extreme value consistency, for example, one salient feature corresponds to five confidence levels/reactions. The characteristic ratio range of the speed grade is [a1,b1], [a2,b2], [a3,b3], [a4,b4], [a5,b5], if the confidence level/reaction speed grade is on this significant feature Monotonically increasing, that is, the higher the level of confidence/reaction speed, the larger the maximum and minimum value of the feature ratio corresponding to the salient feature. If the range of feature ratio satisfies a1<=a2<=a3<=a4<=a5, b1<=b2<=b3<=b4<=b5. At this time, it can be determined that the range of characteristic ratios of different confidence levels/reaction speed grades conforms to the extreme value consistency. According to the salient features of the plurality of second characteristics, the plurality of confidence/reaction speed grades, and the feature value range corresponding to each of the confidence/reaction speed grades, a confidence/reaction speed determination model is generated.

Optionally, if the feature ratio range of different confidence/reaction speed grades does not meet the extreme consistency, the feature value range needs to be changed. For example, the salient features in the above example correspond to the features in the five confidence/reaction speed grades The value range is [a1,b1],[a2,b2],[a3,b3],[a4,b4],[a5,b5], the confidence level/reaction speed grade is monotonically increasing in this significant feature, if A certain characteristic ratio range does not satisfy a1<=a2<=a3<=a4<=a5, b1<=b2<=b3<=b4<=b5, it is necessary to change the characteristic value range to the next grade characteristic value range For example: a1>a2<=a3<=a4<=a5, then the value of a1 needs to be changed to the value of a2, so that a1<=a2<=a3<=a4<=a5 holds true.

It should be understood that more grades or fewer grades can also be pre-divided, which is not specifically limited in this application.

S12: Slice the answer speech of each question to obtain multiple speech fragments.

After the interviewer answers each question, the interviewer's answer speech for each question is divided into multiple speech fragments.

Exemplarily, the answer voice of each question of the interviewer is divided into 28 voice fragments.

S13: Calculate the volume characteristic, the speaking rate characteristic, the duration, and the intermittent duration of each question according to the multiple speech fragments.

The volume feature refers to the size of the interviewer's voice when answering questions.

The speaking rate feature refers to the speed of the interviewer in answering questions, and the amount of voice content per unit time.

The duration refers to the length of time that the interviewer continuously speaks when answering questions.

The intermittent duration refers to the length of time that the interviewer does not speak when answering questions.

Each voice segment has four related features: volume feature, speaking rate feature, duration, and intermittent duration. After averaging the related features of all voice segments of the same question, you can get the relevant feature of each question. Mean. Specifically, the volume characteristics of the multiple speech fragments of each question are averaged to obtain the mean value of the volume characteristics of each question; the speech rate characteristics of the multiple speech fragments of each question are averaged to obtain each question The mean value of the speech rate characteristics; average the duration of the multiple speech fragments of each question to obtain the mean duration of each question; average the discontinuous duration of the multiple speech fragments of each question to obtain each The mean duration of the problem. That is, the volume feature, the speech rate feature, the duration and the intermittent duration obtained from the multiple speech segments all refer to the average value.

S14: Determine the emotional stability of the interviewer according to the volume characteristics of each question.

Generally speaking, the size of the sound can reflect the emotional stability of a person. The greater the fluctuation of the sound, the more agitated the person's emotions; the smaller the fluctuation of the sound, the more stable the emotions of the person. Therefore, the emotional stability of the interviewer can be determined by the distribution of the interviewer's volume characteristics.

Preferably, the determining the emotional stability of the interviewer according to the volume characteristics of each question includes:

Acquiring the maximum volume feature and the minimum volume feature among the volume features of the problem;

Calculate the average volume characteristics of all questions;

Calculating a volume feature amplitude value between the maximum volume feature and the minimum volume feature;

Determine the volume fluctuation range of each question according to the percentage of the absolute value of the difference between the volume characteristic of each question and the average volume characteristic of all the questions in the volume characteristic amplitude value;

Determine the emotional stability of the interviewee based on the average of the volume fluctuations of all questions.

Correspondences between different volume characteristic amplitude values and emotional stability are preset. Once the interviewer’s volume characteristic amplitude values are determined, the emotional stability of the interviewer can be matched according to the correspondence.

Exemplarily, suppose that the maximum volume feature of all questions is max, the minimum volume feature is min, and the average volume feature of all questions is avg, and the volume feature of each question is ai, then the volume fluctuation range of each question Is |ai-avg|/(max-min), and then calculate the average of the volume fluctuations of all the questions to get the average volume fluctuations of all the questions. If the average volume fluctuation range is less than 20%, it is determined that the interviewer’s emotional stability is the first degree of stability, indicating that the interviewer’s emotional stability is "high"; if the average volume fluctuation range is between 20%-30%, The emotional stability of the interviewer is determined to be the second degree of stability, indicating that the emotional stability of the interviewer is "medium". If the average volume fluctuation is greater than 30%, the emotional stability of the interviewer is determined to be the third degree of stability , Indicating that the interviewee’s emotional stability is "low".

S15: Use a pre-built confidence level determination model to determine the speech rate feature, interruption duration, and duration, and determine the interviewer's confidence level.

The more confident the person is, the faster the speech, the shorter the break, and the longer the duration; the less confident the person, the slower the speech, the longer the break, and the shorter the duration.

Preferably, the use of a pre-built confidence level determination model to determine the speech rate feature, interruption duration, and duration duration, and determining the confidence level of the interviewer includes:

Use a pre-built confidence judgment model to judge the speech rate characteristics, interruption time and duration of each question, and determine the confidence level of each question;

Convert the confidence level obtained from all questions into numerical values;

Take the average of the confidence level data of all questions;

The average is rounded up to get the interviewer’s confidence judgment result.

Exemplarily, assuming that a pre-built confidence level determination model is used to determine the speech rate characteristics, interruption duration and duration of each question, the confidence levels of the five questions are determined as follows: Question 1-Confidence level A, Question 2-Confidence Level B, Question 3-Confidence Level B, Question 4-Confidence Level B, Question 5-Confidence Level A, sort the confidence levels corresponding to the 5 questions according to the serial number of the question. ABBBA finally determines that the center position in ABBBA is B, and the target confidence level is B, as the final judgment result of the confidence of the interviewer in the interview process.

In order to avoid an even number of questions failing to determine the interviewer's confidence judgment results, the scores of all questions can be converted into numerical values, and the numerical conversion results are averaged and rounded up (larger) to obtain a personal grade. For example: Question 1-Confidence Level A-5 points, Question 2-Confidence Level B-4 points, Question 3-Confidence Level B-4 points, Question 4-Confidence Level B-4 points, Question 5-Confidence Level Grade A-5 points, the average is 4.4, and the score is 5 points after rounding up (larger), then the interviewer's confidence level judgment result is Grade A.

In an optional embodiment, the use of a pre-built confidence determination model to determine the speech rate characteristics, interruption duration, and duration of each question, and determining the confidence level of each question includes:

Using a pre-built confidence determination model to respectively identify the characteristic range corresponding to the confidence level to which the speech rate feature, the interruption duration, and the duration belong;

Determining the confidence level corresponding to the characteristic range that belongs to the first confidence level of the speaking rate feature, the second confidence level of the intermittent duration, and the third confidence level of the continuous duration;

Judging whether there are multiple levels of the first confidence level, the second confidence level, and the third confidence level;

If there are multiple levels of the first confidence level, the second confidence level, and the third confidence level and they are all the same, determine the multiple same levels as the candidate confidence level;

Sorting the multiple candidate confidence levels in descending order of confidence level to obtain a confidence level ranking queue;

Based on the law of large numbers, it is determined that the confidence level of the target candidate in the confidence level ranking queue is the confidence level of the problem.

In this optional embodiment, each confidence level in any feature box diagram (speaking rate feature box diagram, intermittent duration box diagram, duration box diagram) determines a feature range (range Is the maximum and minimum of different levels), only when all the characteristics of a certain question (characteristics of speech rate, interruption duration, duration) are determined to be the same level, the confidence level of the question is determined to be this level . Exemplarily, suppose that the speech rate feature of a speech is 3.4, the interval length is 1.3, and the duration is 5.6. The speech rate feature range of grade B in the speech rate feature box chart is [3.2,4], and the interval time box chart The interval duration range of the middle level B is [0.8, 1.5], and the interval duration range of the B level in the duration box chart is [5.3, 5.7]. Because of the characteristics of speech rate, interval length and duration, all satisfy the range of level B. Therefore, the confidence level of this question is judged as B level for the first time.

Exemplarily, if the first confidence level is A and B, the second confidence is A and B, and the third confidence is A and B, that is, the first confidence level, There are multiple second confidence level and third confidence level and multiple first confidence level, second confidence level and third confidence level are all the same, then there are multiple candidate confidence levels: A Level and B level, the confidence level sorting queue is AB, and the target candidate’s confidence level is determined to be B level based on the law of large numbers, as the confidence level of the problem.

For another example, the first confidence level is A, B, and C, the second confidence is A, B, and C, and the third confidence is A, B, and C. If there are multiple levels of the first confidence level, the second confidence level, and the third confidence level, and the multiple first, second, and third confidence levels are all the same, the candidate confidence level There are multiple levels: level A, level B, and level C. The confidence level ranking queue is ABC. Based on the law of large numbers, the confidence level of the target candidate is determined as level B as the confidence level of the problem.

It should be understood that since the characteristic range of each grade conforms to extreme value consistency, there will be no intermediate gear disconnection such as ABD or BCE.

In an optional embodiment, if the first self-confidence level, the second self-confidence level, and the third self-confidence level are multiple and are not uniformly the same, the method further includes:

Determine whether the multiple levels of the first confidence level, the second confidence level and the third confidence level have the same level;

If there are the same grades, the same grades are determined as candidate confidence grades.

For example, suppose the first confidence level is A, B, and D, the second confidence is A, B, and E, and the third confidence is A, B, and C. That is, the first, second, and third confidence levels are multiple and the multiple first, second, and third confidence levels are not the same, but the first Confidence, second confidence, and third confidence have the same grades A and B, then the same grades A and B are used as candidate confidence grades, and finally the confidence grade of the problem is determined based on the law of large numbers For the B grade.

In an optional embodiment, if the first self-confidence level, the second self-confidence level, and the third self-confidence level are one and are not uniformly the same, the method further includes:

It is determined that the confidence level of the question is the neutral level.

Wherein, the neutral grade refers to the grade when all the grades are not met after traversing.

Suppose that for a certain problem, use the pre-built confidence determination model to determine that the confidence level corresponding to the speech rate feature of the question is grade A, and use the pre-built confidence determination model to determine the confidence level corresponding to the intermittent duration of the question Level B, using the pre-built confidence determination model to determine that the duration of the question corresponds to the level of confidence level A, because the question’s speech rate characteristics, interruption duration, and duration are not all of the same confidence level, then It is determined that the confidence level of the question does not belong to the A level and does not belong to the B level, that is, there is no situation where the first, second and third confidence levels are the same at the same time, then the confidence level of the question is determined to be the neutral level.

In order to facilitate the calculation of the problem of the neutral level, and at the same time, according to the law of large numbers, the problem of the neutral level is most likely to belong to the most general situation, that is, the C level, so the neutral level can be preset as the C level.

S16: Use a pre-built confidence determination model to determine the speech rate feature and the interruption duration, and determine the interviewer's response speed.

The faster the response speed of the population, the greater the overall speaking rate characteristics, and the shorter the interruption duration; the slower the response speed, the lower the overall speaking rate characteristics, and the longer the interruption duration.

Regarding the process of using the pre-built confidence determination model to determine the speech rate characteristics and the intermittent duration to determine the interviewer’s response speed, the same as the use of the pre-built confidence determination model to determine the speaking rate The process of judging characteristics, intermittent duration, and duration to determine the confidence of the interviewer is detailed in S15 and related descriptions, which will not be elaborated here in this application.

In an optional embodiment, the S15 and the S16 are executed in parallel.

In this alternative embodiment, two threads can be started for synchronous execution at the same time. One thread is used to determine the speech rate feature, interruption duration, and duration using a pre-built confidence determination model, and the other thread uses To use a pre-built reaction speed judgment model to judge the speech rate characteristics and the length of the interruption. Since the two threads are executed in parallel, it can improve the interviewer's confidence and response speed judgment efficiency, shorten the judgment time, and improve the efficiency of interview screening.

S17: Output the interview result of the interviewer according to the emotional stability, reaction speed, and confidence.

In the interview process, after the interviewer’s voice analysis of the interviewer’s answer to the question, the interviewer’s emotional stability, reaction speed, and confidence, the interviewer can be selected according to the focus of the interview position to meet the interview requirements.

For example, for customer service positions, people who are emotionally stable and quick to respond are needed to cope with the ever-changing market. When selecting interviewers, focus on emotional stability and reaction speed.

For another example, for marketing positions, people with a high degree of confidence are needed to give customers a positive impression and promote cooperation between the two parties. When selecting interviewers, focus on self-confidence.

In summary, the voice-based interviewer judgment method described in this application obtains the answer voice of each question of the interviewer, slices the answer voice of each question, and obtains multiple voice fragments, and extracts each of the voices. The volume characteristics, speaking rate characteristics, duration, and intermittent duration of the fragments are used to determine the emotional stability of the interviewer based on the volume characteristics, and then the pre-built confidence determination model and reaction speed determination model are used to determine the speech rate characteristics and duration. , Intermittent time judgment, determine the confidence and reaction speed of the interviewer, and output the interview result of the interviewer according to the emotional stability, reaction speed and confidence. This application uses in-depth analysis and mining of the human-computer interaction voice during the interview process to determine multiple characteristics of the interviewer, such as emotional stability, reaction speed, and self-confidence. Through these characteristics, the interviewer can be evaluated objectively and comprehensively. The result is more precise and accurate, which improves the efficiency and quality of the interview judgment.

It can be seen from the above embodiments that this application can be applied in fields such as smart government affairs, so as to promote the development of smart cities.

Example two

Fig. 2 is a structural diagram of a voice-based interviewer judging device provided in the second embodiment of the present application.

In some embodiments, the voice-based interviewer determination device 20 may include multiple functional modules composed of computer-readable instruction segments. The computer-readable instructions of each program segment in the voice-based interviewer determination device 20 may be stored in the memory of the terminal and executed by the at least one processor to execute (see FIG. 1 for details). The function of the interviewer's judgment.

In this embodiment, the voice-based interviewer determination device 20 can be divided into multiple functional modules according to the functions it performs. The functional modules may include: an acquisition module 201, a construction module 202, a slicing module 203, a calculation module 204, a first determination module 205, a second determination module 206, a third determination module 207, and an output module 208. The module referred to in this application refers to a series of computer-readable instruction segments that can be executed by at least one processor and can complete fixed functions, and are stored in a memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

The obtaining module 201 is used to obtain the answer voices of multiple questions of the interviewer.

Set up multiple questions in advance according to the needs of the recruitment position, and obtain the voice of the interviewer and the machine for each question in the interview process through human-computer interaction, and then separate the question voice issued by the machine from the interviewer’s answer voice , And finally screen out the interviewer’s answer voice.

As an optional embodiment, before the obtaining the answer voices of the multiple questions of the interviewer, the apparatus further includes:

The construction module 202 is used to construct a confidence degree judgment model and a reaction speed judgment model.

Wherein, the process of constructing the confidence level judgment model and the reaction speed judgment model includes:

Obtain multiple sample voices;

Extracting multiple features in the multiple sample voices;

According to the distribution of the multiple features, the first significant feature with a large degree of confidence and the second significant feature with a large degree of response speed are selected from the multiple features, wherein the first significant Features include: speech rate characteristics, duration, and intermittent duration, and the second significant feature includes: speech rate features, intermittent duration;

Determine the multiple confidence levels corresponding to the multiple first salient features and the feature range corresponding to each of the confidence levels, and determine the multiple response speed levels corresponding to the multiple second salient features and each Describe the characteristic range corresponding to the reaction speed grade;

Judge whether the characteristic ranges of different confidence levels and the characteristic ranges of different reaction speed levels are consistent with extreme values;

If the feature ranges of different confidence levels are consistent with extreme values, construct a confidence determination model based on the multiple first salient features, multiple confidence levels, and the feature range corresponding to each of the confidence levels;

If the characteristic ranges of different reaction speed grades meet the extreme consistency, a reaction speed determination model is constructed based on the plurality of second salient characteristics, the plurality of reaction speed grades, and the characteristic range corresponding to each of the reaction speed grades.

Through a large number of experiments: the self-confidence, emotional stability, and reaction speed of the sample speech of each question answered by multiple interviewers are labeled, and then the four relevant features and the corresponding labeling results are used as the learning object to establish a learning model , Found that: from the data distribution of each relevant feature in different degrees of confidence/emotional stability/reaction speed, the data distribution of people with different confidence/emotional stability/reaction speed is obvious and regular, thus The interviewer's confidence, emotional stability, and reaction speed can be quantitatively evaluated through four relevant characteristics of the interviewer: volume characteristics, speaking rate characteristics, duration and intermittent duration.

Through observation, we found the distribution of four different volume characteristics, speaking rate characteristics, duration, and intermittent duration at different confidence levels and different reaction speeds, and determined the characteristic types with different degrees of confidence and different reaction speeds. A feature type with a relatively large degree of discrimination. According to the four relevant features and confidence levels of the sample speech, generate the first box plots of each relevant feature at different confidence levels and the second box plots of each relevant feature at different reaction speed levels, and start from the first The box chart identifies several first notable features that have a greater degree of discrimination in different levels of confidence: speaking rate characteristics, duration, and intermittent duration, and the second box chart determines the degree of discrimination between different levels of reaction speed The relatively large second significant features: the characteristics of speech rate, the length of the interruption. Finally, a self-confidence judgment model is constructed based on the three first salient features of speech rate, duration, and intermittent duration. A response speed judgment model is constructed based on the two second salient features of speech rate and intermittent duration.

Wherein, the first box diagram is generated from the distribution of the eigenvalues of the first salient feature at different confidence levels, and the second box diagram is generated from the distribution of the eigenvalues of the second salient feature at different reaction speed levels. of.

In the embodiment of the present application, when training the salient feature, it is necessary to determine the salient feature corresponding to the salient feature according to the maximum and minimum values corresponding to the salient feature in the box diagrams of different confidence/reaction speed levels. The range of characteristic values in different levels of confidence/reaction speed. After determining the feature value range corresponding to the salient feature at different confidence levels/reaction speed grades, it is necessary to determine whether the feature value range conforms to the extreme value consistency, for example, one salient feature corresponds to five confidence levels/reactions. The characteristic ratio range of the speed grade is [a1,b1], [a2,b2], [a3,b3], [a4,b4], [a5,b5], if the confidence level/reaction speed grade is on this significant feature Monotonically increasing, that is, the higher the level of confidence/reaction speed, the larger the maximum and minimum value of the feature ratio corresponding to the salient feature. If the range of feature ratio satisfies a1<=a2<=a3<=a4<=a5, b1<=b2<=b3<=b4<=b5. At this time, it can be determined that the range of characteristic ratios of different confidence levels/reaction speed grades conforms to the extreme value consistency. According to the salient features of the plurality of second characteristics, the plurality of confidence/reaction speed grades, and the feature value range corresponding to each of the confidence/reaction speed grades, a confidence/reaction speed determination model is generated.

Optionally, if the feature ratio range of different confidence/reaction speed grades does not meet the extreme consistency, the feature value range needs to be changed. For example, the salient features in the above example correspond to the features in the five confidence/reaction speed grades The value range is [a1,b1],[a2,b2],[a3,b3],[a4,b4],[a5,b5], the confidence level/reaction speed grade is monotonically increasing in this significant feature, if A certain characteristic ratio range does not satisfy a1<=a2<=a3<=a4<=a5, b1<=b2<=b3<=b4<=b5, it is necessary to change the characteristic value range to the next grade characteristic value range For example: a1>a2<=a3<=a4<=a5, then the value of a1 needs to be changed to the value of a2, so that a1<=a2<=a3<=a4<=a5 holds true.

It should be understood that more grades or fewer grades can also be pre-divided, which is not specifically limited in this application.

The slicing module 203 is used to slice the answer speech of each question to obtain multiple speech fragments.

After the interviewer answers each question, the interviewer's answer speech for each question is divided into multiple speech fragments.

Exemplarily, the answer voice of each question of the interviewer is divided into 28 voice fragments.

The calculation module 204 is configured to calculate the volume characteristic, the speaking rate characteristic, the duration, and the intermittent duration of each question according to the multiple speech fragments.

The volume feature refers to the size of the interviewer's voice when answering questions.

The speaking rate feature refers to the speed of the interviewer in answering questions, and the amount of voice content per unit time.

The duration refers to the length of time that the interviewer continuously speaks when answering questions.

The intermittent duration refers to the length of time that the interviewer does not speak when answering questions.

Each voice segment has four related features: volume feature, speaking rate feature, duration, and intermittent duration. After averaging the related features of all voice segments of the same question, you can get the relevant feature of each question. Mean. Specifically, the volume characteristics of the multiple speech fragments of each question are averaged to obtain the mean value of the volume characteristics of each question; the speech rate characteristics of the multiple speech fragments of each question are averaged to obtain each question The mean value of the speech rate characteristics; average the duration of the multiple speech fragments of each question to obtain the mean duration of each question; average the discontinuous duration of the multiple speech fragments of each question to obtain each The mean duration of the problem. That is, the volume feature, the speech rate feature, the duration and the intermittent duration obtained from the multiple speech segments all refer to the average value.

The first determining module 205 is configured to determine the emotional stability of the interviewer according to the volume characteristics of each question.

Generally speaking, the size of the sound can reflect the emotional stability of a person. The greater the fluctuation of the sound, the more agitated the person's emotions; the smaller the fluctuation of the sound, the more stable the emotions of the person. Therefore, the emotional stability of the interviewer can be determined by the distribution of the interviewer's volume characteristics.

Preferably, the first determining module 205 determining the emotional stability of the interviewer according to the volume characteristics of each question includes:

Acquiring the maximum volume feature and the minimum volume feature among the volume features of the problem;

Calculate the average volume characteristics of all questions;

Calculating a volume feature amplitude value between the maximum volume feature and the minimum volume feature;

Determine the volume fluctuation range of each question according to the percentage of the absolute value of the difference between the volume characteristic of each question and the average volume characteristic of all the questions in the volume characteristic amplitude value;

Determine the emotional stability of the interviewee based on the average of the volume fluctuations of all questions.

Correspondences between different volume characteristic amplitude values and emotional stability are preset. Once the interviewer’s volume characteristic amplitude values are determined, the emotional stability of the interviewer can be matched according to the correspondence.

Exemplarily, suppose that the maximum volume feature of all questions is max, the minimum volume feature is min, and the average volume feature of all questions is avg, and the volume feature of each question is ai, then the volume fluctuation range of each question Is |ai-avg|/(max-min), and then calculate the average of the volume fluctuations of all the questions to get the average volume fluctuations of all the questions. If the average volume fluctuation range is less than 20%, it is determined that the interviewer’s emotional stability is the first degree of stability, indicating that the interviewer’s emotional stability is "high"; if the average volume fluctuation range is between 20%-30%, The emotional stability of the interviewer is determined to be the second degree of stability, indicating that the emotional stability of the interviewer is "medium". If the average volume fluctuation is greater than 30%, the emotional stability of the interviewer is determined to be the third degree of stability , Indicating that the interviewee’s emotional stability is "low".

The second determining module 206 is configured to use a pre-built confidence level determination model to determine the speech rate feature, interruption duration, and duration duration, and determine the confidence level of the interviewer.

The more confident the person is, the faster the speech, the shorter the break, and the longer the duration; the less confident the person, the slower the speech, the longer the break, and the shorter the duration.

Preferably, the second determining module 206 uses a pre-built confidence level determination model to determine the speech rate feature, interruption duration, and duration, and determining the interviewer’s confidence level includes:

Use a pre-built confidence judgment model to judge the speech rate characteristics, interruption time and duration of each question, and determine the confidence level of each question;

Convert the confidence level obtained from all questions into numerical values;

Take the average of the confidence level data of all questions;

The average is rounded up to get the interviewer’s confidence judgment result.

Exemplarily, assuming that a pre-built confidence level determination model is used to determine the speech rate characteristics, interruption duration and duration of each question, the confidence levels of the five questions are determined as follows: Question 1-Confidence level A, Question 2-Confidence Level B, Question 3-Confidence Level B, Question 4-Confidence Level B, Question 5-Confidence Level A, sort the confidence levels corresponding to the 5 questions according to the serial number of the question. ABBBA finally determines that the center position in ABBBA is B, and the target confidence level is B, as the final judgment result of the confidence of the interviewer in the interview process.

In order to avoid an even number of questions failing to determine the interviewer's confidence judgment results, the scores of all questions can be converted into numerical values, and the numerical conversion results are averaged and rounded up (larger) to obtain a personal grade. For example: Question 1-Confidence Level A-5 points, Question 2-Confidence Level B-4 points, Question 3-Confidence Level B-4 points, Question 4-Confidence Level B-4 points, Question 5-Confidence Level Grade A-5 points, the average is 4.4, and the score is 5 points after rounding up (larger), then the interviewer's confidence level judgment result is Grade A.

In an optional embodiment, the use of a pre-built confidence determination model to determine the speech rate characteristics, interruption duration, and duration of each question, and determining the confidence level of each question includes:

Using a pre-built confidence determination model to respectively identify the characteristic range corresponding to the confidence level to which the speech rate feature, the interruption duration, and the duration belong;

Determining the confidence level corresponding to the characteristic range that belongs to the first confidence level of the speaking rate feature, the second confidence level of the intermittent duration, and the third confidence level of the continuous duration;

Judging whether there are multiple levels of the first confidence level, the second confidence level, and the third confidence level;

If there are multiple levels of the first confidence level, the second confidence level, and the third confidence level and they are all the same, determine the multiple same levels as the candidate confidence level;

Sorting the multiple candidate confidence levels in descending order of confidence level to obtain a confidence level ranking queue;

Based on the law of large numbers, it is determined that the confidence level of the target candidate in the confidence level ranking queue is the confidence level of the problem.

In this optional embodiment, each confidence level in any feature box diagram (speaking rate feature box diagram, intermittent duration box diagram, duration box diagram) determines a feature range (range Is the maximum and minimum of different levels), only when all the characteristics of a certain question (characteristics of speech rate, interruption duration, duration) are determined to be the same level, the confidence level of the question is determined to be this level . Exemplarily, suppose that the speech rate feature of a speech is 3.4, the interval length is 1.3, and the duration is 5.6. The speech rate feature range of grade B in the speech rate feature box chart is [3.2,4], and the interval time box chart The interval duration range of the middle level B is [0.8, 1.5], and the interval duration range of the B level in the duration box chart is [5.3, 5.7]. Because of the characteristics of speech rate, interval length and duration, all satisfy the range of level B. Therefore, the confidence level of this question is judged as B level for the first time.

Exemplarily, if the first confidence level is A and B, the second confidence is A and B, and the third confidence is A and B, that is, the first confidence level, There are multiple second confidence level and third confidence level and multiple first confidence level, second confidence level and third confidence level are all the same, then there are multiple candidate confidence levels: A Level and B level, the confidence level sorting queue is AB, and the target candidate’s confidence level is determined to be B level based on the law of large numbers, as the confidence level of the problem.

For another example, the first confidence level is A, B, and C, the second confidence is A, B, and C, and the third confidence is A, B, and C. If there are multiple levels of the first confidence level, the second confidence level, and the third confidence level, and the multiple first, second, and third confidence levels are all the same, the candidate confidence level There are multiple levels: level A, level B, and level C. The confidence level ranking queue is ABC. Based on the law of large numbers, the confidence level of the target candidate is determined as level B as the confidence level of the problem.

It should be understood that since the characteristic range of each grade conforms to extreme value consistency, there will be no intermediate gear disconnection such as ABD or BCE.

In an optional embodiment, if the first self-confidence level, the second self-confidence level, and the third self-confidence level are multiple and are not uniformly the same, the device further includes:

The judgment module is used to judge whether the multiple levels of the first confidence level, the second confidence level, and the third confidence level have the same level;

The judgment module is also used to determine the same grade as the candidate confidence grade if there are the same grades.

For example, suppose the first confidence level is A, B, and D, the second confidence is A, B, and E, and the third confidence is A, B, and C. That is, the first, second, and third confidence levels are multiple and the multiple first, second, and third confidence levels are not the same, but the first Confidence, second confidence, and third confidence have the same grades A and B, then the same grades A and B are used as candidate confidence grades, and finally the confidence grade of the problem is determined based on the law of large numbers For the B grade.

In an optional embodiment, if the first confidence level, the second confidence level, and the third confidence level are one and are not uniformly the same, the third determining module 207 is further configured to determine The confidence level of the problem is neutral.

Wherein, the neutral grade refers to the grade when all the grades are not met after traversing.

Suppose that for a certain problem, use the pre-built confidence determination model to determine that the confidence level corresponding to the speech rate feature of the question is grade A, and use the pre-built confidence determination model to determine the confidence level corresponding to the intermittent duration of the question Level B, using the pre-built confidence determination model to determine that the duration of the question corresponds to the level of confidence level A, because the question’s speech rate characteristics, interruption duration, and duration are not all of the same confidence level, then It is determined that the confidence level of the question does not belong to the A level and does not belong to the B level, that is, there is no situation where the first, second and third confidence levels are the same at the same time, then the confidence level of the question is determined to be the neutral level.

In order to facilitate the calculation of the problem of the neutral level, and at the same time, according to the law of large numbers, the problem of the neutral level is most likely to belong to the most general situation, that is, the C level, so the neutral level can be preset as the C level.

The third determining module 207 is further configured to use a pre-built confidence level determination model to determine the speech rate characteristics and interruption duration, and determine the interviewer's response speed.

The faster the response speed of the population, the greater the overall speaking rate characteristics, and the shorter the interruption duration; the slower the response speed, the lower the overall speaking rate characteristics, and the longer the interruption duration.

Regarding the process of using the pre-built confidence determination model to determine the speech rate characteristics and the intermittent duration to determine the interviewer’s response speed, the same as the use of the pre-built confidence determination model to determine the speaking rate The process of judging characteristics, intermittent duration, and duration to determine the confidence of the interviewer is detailed in S15 and related descriptions, which will not be elaborated here in this application.

In an optional embodiment, the second determining module 206 and the third determining module 207 are executed in parallel.

In this alternative embodiment, two threads can be started for synchronous execution at the same time. One thread is used to determine the speech rate feature, interruption duration, and duration using a pre-built confidence determination model, and the other thread uses To use a pre-built reaction speed judgment model to judge the speech rate characteristics and the length of the interruption. Since the two threads are executed in parallel, it can improve the interviewer's confidence and response speed judgment efficiency, shorten the judgment time, and improve the efficiency of interview screening.

The output module 208 is configured to output the interview result of the interviewer according to the emotional stability, reaction speed, and confidence.

In the interview process, after the interviewer’s voice analysis of the interviewer’s answer to the question, the interviewer’s emotional stability, reaction speed, and confidence, the interviewer can be selected according to the focus of the interview position to meet the interview requirements.

For example, for customer service positions, people who are emotionally stable and quick to respond are needed to cope with the ever-changing market. When selecting interviewers, focus on emotional stability and reaction speed.

For another example, for marketing positions, people with a high degree of confidence are needed to give customers a positive impression and promote cooperation between the two parties. When selecting interviewers, focus on self-confidence.

In summary, the voice-based interviewer judgment device described in this application obtains the answer voice of each question of the interviewer, slices the answer voice of each question, and obtains multiple voice fragments, and extracts each of the voices. The volume characteristics, speaking rate characteristics, duration, and intermittent duration of the fragments are used to determine the emotional stability of the interviewer based on the volume characteristics, and then the pre-built confidence determination model and reaction speed determination model are used to determine the speech rate characteristics and duration. , Intermittent time judgment, determine the confidence and reaction speed of the interviewer, and output the interview result of the interviewer according to the emotional stability, reaction speed and confidence. This application uses in-depth analysis and mining of the human-computer interaction voice during the interview process to determine multiple characteristics of the interviewer, such as emotional stability, reaction speed, and self-confidence. Through these characteristics, the interviewer can be evaluated objectively and comprehensively. The result is more precise and accurate, which improves the efficiency and quality of the interview judgment.

It can be seen from the above embodiments that this application can be applied in fields such as smart government affairs, so as to promote the development of smart cities.

Example three

Refer to FIG. 3, which is a schematic structural diagram of a terminal provided in Embodiment 3 of this application. In a preferred embodiment of the present application, the terminal 3 includes a memory 31, at least one processor 32, at least one communication bus 33, and a transceiver 34.

Those skilled in the art should understand that the structure of the terminal shown in FIG. 3 does not constitute a limitation of the embodiment of the present application. It may be a bus-type structure or a star structure. The terminal 3 may also include more More or less other hardware or software, or different component arrangements.

In some embodiments, the terminal 3 is a device that can automatically perform numerical calculation and/or information processing according to pre-set or stored instructions. Its hardware includes but is not limited to a microprocessor, an application specific integrated circuit, and Programming gate arrays, digital processors and embedded devices, etc. The terminal 3 may also include client equipment. The client equipment includes, but is not limited to, any electronic product that can interact with the client through a keyboard, a mouse, a remote control, a touch panel, or a voice control device, for example, a personal computer. Computers, tablets, smart phones, digital cameras, etc.

It should be noted that the terminal 3 is only an example. If other existing or future electronic products can be adapted to this application, they should also be included in the protection scope of this application and included here by reference.

In some embodiments, the memory 31 is used to store computer-readable instructions and various data, such as a device installed in the terminal 3, and realize high-speed and automatic completion of programs or data during the operation of the terminal 3 Access. The memory 31 includes volatile and non-volatile memory, for example, random access memory (Random Access Memory, RAM), read-only memory (Read-Only Memory, ROM), and programmable read-only memory (Programmable Read-Only Memory). Only Memory, PROM), Erasable Programmable Read-Only Memory (EPROM), One-time Programmable Read-Only Memory (OTPROM), and electronically erasable rewritable Read-only memory (Electrically-Erasable Programmable Read-Only Memory, EEPROM), CD-ROM (Compact Disc Read-Only Memory, CD-ROM) or other optical disk storage, magnetic disk storage, tape storage, or capable of carrying or storing data Any other medium readable by the computer. The computer-readable storage medium may be non-volatile or volatile.

In some embodiments, the at least one processor 32 may be composed of integrated circuits, for example, may be composed of a single packaged integrated circuit, or may be composed of multiple integrated circuits with the same function or different functions, including one Or a combination of multiple central processing units (CPU), microprocessors, digital processing chips, graphics processors, and various control chips. The at least one processor 32 is the control core (Control Unit) of the terminal 3. Various interfaces and lines are used to connect the various components of the entire terminal 3, and by running or executing programs or modules stored in the memory 31, And call the data stored in the memory 31 to execute various functions of the terminal 3 and process data.

In some embodiments, the at least one communication bus 33 is configured to implement connection and communication between the memory 31 and the at least one processor 32 and the like.

Although not shown, the terminal 3 may also include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to the at least one processor 32 through a power management device, so as to realize management through the power management device. Functions such as charging, discharging, and power management. The power supply may also include any components such as one or more DC or AC power supplies, recharging devices, power failure detection circuits, power converters or inverters, and power status indicators. The terminal 3 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

It should be understood that the embodiments are only for illustrative purposes, and are not limited by this structure in the scope of the patent application.

The above-mentioned integrated unit implemented in the form of a software function module may be stored in a computer readable storage medium. The above-mentioned software function module is stored in a storage medium and includes several instructions to make a computer device (which may be a personal computer, a terminal, or a network device, etc.) or a processor execute the method described in each embodiment of the present application. section.

In a further embodiment, with reference to FIG. 2, the at least one processor 32 can execute the operating device of the terminal 3 and various installed applications, computer-readable instructions, etc., such as the above-mentioned modules.

The memory 31 stores computer-readable instructions, and the at least one processor 32 can call the computer-readable instructions stored in the memory 31 to perform related functions. For example, the various modules described in FIG. 2 are computer-readable instructions stored in the memory 31 and executed by the at least one processor 32, so as to realize the functions of the various modules.

In an embodiment of the present application, the memory 31 stores multiple instructions, and the multiple instructions are executed by the at least one processor 32 to implement all or part of the steps in the method described in the present application.

Specifically, for the specific implementation method of the at least one processor 32 on the foregoing instructions, reference may be made to the description of the relevant steps in the embodiment corresponding to FIG. 1, which will not be repeated here.

In the several embodiments provided in this application, it should be understood that the disclosed device and method can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

The modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the objectives of the solutions of the embodiments.

In addition, the functional modules in the various embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware, or may be implemented in the form of hardware plus software functional modules.

For those skilled in the art, it is obvious that the present application is not limited to the details of the foregoing exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or basic characteristics of the application. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of this application is defined by the appended claims rather than the above description, and therefore it is intended to fall into the claims. All changes in the meaning and scope of the equivalent elements of are included in this application. Any reference signs in the claims should not be regarded as limiting the claims involved. In addition, it is obvious that the word "including" does not exclude other elements or, and the singular does not exclude the plural. Multiple units or devices stated in the device claims can also be implemented by one unit or device through software or hardware. Words such as first and second are used to denote names, but do not denote any specific order.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the application and not to limit them. Although the application has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the application can be Make modifications or equivalent replacements without departing from the spirit and scope of the technical solution of the present application.

Claims (22)

1. A voice-based interviewer judgment method, wherein the method includes:

   Obtain the answer voice of the interviewer's multiple questions;

   Slice the answer voice of each question to get multiple voice fragments;

   Calculate the volume characteristics, speech rate characteristics, duration, and intermittent duration of each question according to the multiple speech fragments;

   Determine the emotional stability of the interviewer according to the volume characteristics of each question;

   Use a pre-built confidence judgment model to judge the speaking rate feature, the intermittent duration, and the duration to determine the interviewer's confidence;

   Use a pre-built confidence judgment model to judge the speech rate characteristics and the length of the interruption, and determine the interviewer's response speed;

   The interview result of the interviewer is output according to the emotional stability, reaction speed and self-confidence.
2. 8. The method according to claim 1, wherein, before said obtaining the answering voices of a plurality of questions of the interviewer, the method further comprises:

   Establish a self-confidence judgment model and a reaction speed judgment model;

   Wherein, the process of constructing the confidence level judgment model and the reaction speed judgment model includes:

   Obtain multiple sample voices;

   Extracting multiple features in the multiple sample voices;

   According to the distribution of the multiple features, the first significant feature with a large degree of confidence and the second significant feature with a large degree of response speed are selected from the multiple features, wherein the first significant Features include: speech rate characteristics, duration, and intermittent duration, and the second significant feature includes: speech rate features, intermittent duration;

   Determine the multiple confidence levels corresponding to the multiple first salient features and the feature range corresponding to each of the confidence levels, and determine the multiple response speed levels corresponding to the multiple second salient features and each Describe the characteristic range corresponding to the reaction speed grade;

   Judge whether the characteristic ranges of different confidence levels and the characteristic ranges of different reaction speed levels are consistent with extreme values;

   If the feature ranges of different confidence levels are consistent with extreme values, construct a confidence determination model based on the multiple first salient features, multiple confidence levels, and the feature range corresponding to each of the confidence levels;

   If the characteristic ranges of different reaction speed grades meet the extreme consistency, a reaction speed determination model is constructed based on the plurality of second salient characteristics, the plurality of reaction speed grades, and the characteristic range corresponding to each of the reaction speed grades.
3. The method of claim 1, wherein the determining the emotional stability of the interviewer according to the volume characteristics of each question comprises:

   Acquiring the maximum volume feature and the minimum volume feature among the volume features of the problem;

   Calculate the average volume characteristics of all questions;

   Calculating a volume feature amplitude value between the maximum volume feature and the minimum volume feature;

   Determine the volume fluctuation range of each question according to the percentage of the absolute value of the difference between the volume characteristic of each question and the average volume characteristic of all the questions in the volume characteristic amplitude value;

   Determine the emotional stability of the interviewee based on the average of the volume fluctuations of all questions.
4. The method according to any one of claims 1 to 3, wherein the pre-built confidence determination model is used to determine the speaking rate feature, the interruption duration, and the duration to determine the confidence of the interviewer include:

   Use a pre-built confidence judgment model to judge the speech rate characteristics, interruption time and duration of each question, and determine the confidence level of each question;

   Convert the confidence level obtained from all questions into numerical values;

   Take the average of the confidence level data of all questions;

   The average is rounded up to get the interviewer’s confidence judgment result.
5. The method of claim 4, wherein the use of a pre-built confidence determination model to determine the speech rate characteristics, interruption duration, and duration of each question, and determining the confidence level of each question comprises :

   Using a pre-built confidence determination model to respectively identify the characteristic range corresponding to the confidence level to which the speech rate feature, the interruption duration, and the duration belong;

   Determining the confidence level corresponding to the characteristic range that belongs to the first confidence level of the speaking rate feature, the second confidence level of the intermittent duration, and the third confidence level of the continuous duration;

   Judging whether there are multiple levels of the first confidence level, the second confidence level, and the third confidence level;

   If there are multiple levels of the first confidence level, the second confidence level, and the third confidence level and they are all the same, determine the multiple same levels as the candidate confidence level;

   Sorting the multiple candidate confidence levels in descending order of confidence level to obtain a confidence level ranking queue;

   Based on the law of large numbers, it is determined that the confidence level of the target candidate in the confidence level ranking queue is the confidence level of the problem.
6. 5. The method of claim 5, wherein if the first confidence level, the second confidence level, and the third confidence level are multiple and are not uniformly the same, the method further comprises:

   Determine whether the multiple levels of the first confidence level, the second confidence level and the third confidence level have the same level;

   If there are the same grades, the same grades are determined as candidate confidence grades.
7. 5. The method of claim 5, wherein if the first confidence level, the second confidence level, and the third confidence level are one and are not uniformly the same, the method further comprises:

   It is determined that the confidence level of the question is the neutral level.
8. A voice-based interviewer judging device, wherein the device includes:

   The acquisition module is used to acquire the answer voice of the interviewer’s multiple questions

   The slicing module is used to slice the answer voice of each question to obtain multiple voice fragments;

   The calculation module is used to calculate the volume characteristic, the speaking rate characteristic, the duration, and the intermittent duration of each question according to the multiple speech fragments;

   The first determining module is configured to determine the emotional stability of the interviewer according to the volume characteristics of each question;

   The second determination module is configured to use a pre-built confidence determination model to determine the speaking rate feature, the intermittent duration, and the duration, and determine the interviewer's confidence;

   The third determining module is configured to use a pre-built confidence level determination model to determine the speaking rate feature and the interruption duration, and determine the interviewer's response speed;

   The output module is used to output the interview result of the interviewer according to the emotional stability, reaction speed and confidence.
9. A terminal, wherein the terminal includes a processor, and the processor is configured to implement the following steps when executing computer-readable instructions stored in a memory:

   Obtain the answer voice of the interviewer's multiple questions;

   Slice the answer voice of each question to get multiple voice fragments;

   Calculate the volume characteristics, speech rate characteristics, duration, and intermittent duration of each question according to the multiple speech fragments;

   Determine the emotional stability of the interviewer according to the volume characteristics of each question;

   Use a pre-built confidence judgment model to judge the speaking rate feature, the intermittent duration, and the duration to determine the interviewer's confidence;

   Use a pre-built confidence judgment model to judge the speech rate characteristics and the length of the interruption, and determine the interviewer's response speed;

   The interview result of the interviewer is output according to the emotional stability, reaction speed and self-confidence.
10. 9. The terminal according to claim 9, wherein, before said acquiring the answering voices of a plurality of questions of the interviewer, the processor further implements the following steps when executing the computer-readable instructions:

    Establish a self-confidence judgment model and a reaction speed judgment model;

    Wherein, the process of constructing the confidence level judgment model and the reaction speed judgment model includes:

    Obtain multiple sample voices;

    Extracting multiple features in the multiple sample voices;

    According to the distribution of the multiple features, the first significant feature with a large degree of confidence and the second significant feature with a large degree of response speed are selected from the multiple features, wherein the first significant Features include: speech rate characteristics, duration, and intermittent duration, and the second significant feature includes: speech rate features, intermittent duration;

    Determine the multiple confidence levels corresponding to the multiple first salient features and the feature range corresponding to each of the confidence levels, and determine the multiple response speed levels corresponding to the multiple second salient features and each Describe the characteristic range corresponding to the reaction speed grade;

    Judge whether the characteristic ranges of different confidence levels and the characteristic ranges of different reaction speed levels are consistent with extreme values;

    If the feature ranges of different confidence levels are consistent with extreme values, construct a confidence determination model based on the multiple first salient features, multiple confidence levels, and the feature range corresponding to each of the confidence levels;

    If the characteristic ranges of different reaction speed grades meet the extreme consistency, a reaction speed determination model is constructed based on the plurality of second salient characteristics, the plurality of reaction speed grades, and the characteristic range corresponding to each of the reaction speed grades.
11. The terminal according to claim 9, wherein the processor executes the computer-readable instructions to realize the determination of the emotional stability of the interviewer according to the volume characteristics of each question, which specifically includes:

    Acquiring the maximum volume feature and the minimum volume feature among the volume features of the problem;

    Calculate the average volume characteristics of all questions;

    Calculating a volume feature amplitude value between the maximum volume feature and the minimum volume feature;

    Determine the volume fluctuation range of each question according to the percentage of the absolute value of the difference between the volume characteristic of each question and the average volume characteristic of all the questions in the volume characteristic amplitude value;

    Determine the emotional stability of the interviewee based on the average of the volume fluctuations of all questions.
12. The terminal according to any one of claims 9 to 11, wherein the processor executes the computer-readable instructions to implement the use of the pre-built confidence determination model to perform the evaluation of the speech rate characteristics, the intermittent duration, and When determining the duration of the judgment and determining the confidence of the interviewer, it specifically includes:

    Use a pre-built confidence judgment model to judge the speech rate characteristics, interruption time and duration of each question, and determine the confidence level of each question;

    Convert the confidence level obtained from all questions into numerical values;

    Take the average of the confidence level data of all questions;

    The average is rounded up to get the interviewer’s confidence judgment result.
13. The terminal according to claim 12, wherein the processor executes the computer-readable instructions to implement the use of the pre-built confidence determination model for the speech rate characteristics, interruption duration, and duration of each question When making judgments and determining the level of confidence for each question, the details include:

    Using a pre-built confidence determination model to respectively identify the characteristic range corresponding to the confidence level to which the speech rate feature, the interruption duration, and the duration belong;

    Determining the confidence level corresponding to the characteristic range that belongs to the first confidence level of the speaking rate feature, the second confidence level of the intermittent duration, and the third confidence level of the continuous duration;

    Judging whether there are multiple levels of the first confidence level, the second confidence level, and the third confidence level;

    If there are multiple levels of the first confidence level, the second confidence level, and the third confidence level and they are all the same, determine the multiple same levels as the candidate confidence level;

    Sorting the multiple candidate confidence levels in descending order of confidence level to obtain a confidence level ranking queue;

    Based on the law of large numbers, it is determined that the confidence level of the target candidate in the confidence level ranking queue is the confidence level of the problem.
14. The terminal according to claim 13, wherein, if the first confidence level, the second confidence level, and the third confidence level are multiple and are not uniformly the same, the processor executes the computer readable The instruction also implements the following steps:

    Determine whether the multiple levels of the first confidence level, the second confidence level and the third confidence level have the same level;

    If there are the same grades, the same grades are determined as candidate confidence grades.
15. The terminal according to claim 13, wherein, if the first confidence level, the second confidence level, and the third confidence level are one and the same, the processor executes the computer-readable instruction Also implement the following steps:

    It is determined that the confidence level of the question is the neutral level.
16. A computer-readable storage medium having computer-readable instructions stored thereon, wherein the computer-readable instructions implement the following steps when executed by a processor:

    Obtain the answer voice of the interviewer's multiple questions;

    Slice the answer voice of each question to get multiple voice fragments;

    Calculate the volume characteristics, speech rate characteristics, duration, and intermittent duration of each question according to the multiple speech fragments;

    Determine the emotional stability of the interviewer according to the volume characteristics of each question;

    Use a pre-built confidence judgment model to judge the speaking rate feature, the intermittent duration, and the duration to determine the interviewer's confidence;

    Use a pre-built confidence judgment model to judge the speech rate characteristics and the length of the interruption, and determine the interviewer's response speed;

    The interview result of the interviewer is output according to the emotional stability, reaction speed and self-confidence.
17. 16. The computer-readable storage medium according to claim 16, wherein, before said obtaining the answering voices of a plurality of questions of the interviewer, the following steps are further implemented when the computer-readable instructions are executed by the processor:

    Establish a self-confidence judgment model and a reaction speed judgment model;

    Wherein, the process of constructing the confidence level judgment model and the reaction speed judgment model includes:

    Obtain multiple sample voices;

    Extracting multiple features in the multiple sample voices;

    According to the distribution of the multiple features, the first significant feature with a large degree of confidence and the second significant feature with a large degree of response speed are selected from the multiple features, wherein the first significant Features include: speech rate characteristics, duration, and intermittent duration, and the second significant feature includes: speech rate features, intermittent duration;

    Determine the multiple confidence levels corresponding to the multiple first salient features and the feature range corresponding to each of the confidence levels, and determine the multiple response speed levels corresponding to the multiple second salient features and each Describe the characteristic range corresponding to the reaction speed grade;

    Judge whether the characteristic ranges of different confidence levels and the characteristic ranges of different reaction speed levels are consistent with extreme values;

    If the feature ranges of different confidence levels are consistent with extreme values, construct a confidence determination model based on the multiple first salient features, multiple confidence levels, and the feature range corresponding to each of the confidence levels;

    If the characteristic ranges of different reaction speed grades meet the extreme consistency, a reaction speed determination model is constructed based on the plurality of second salient characteristics, the plurality of reaction speed grades, and the characteristic range corresponding to each of the reaction speed grades.
18. The computer-readable storage medium of claim 16, wherein the computer-readable instructions are executed by the processor to implement the determination of the emotional stability of the interviewer according to the volume characteristics of each question , Specifically including:

    Acquiring the maximum volume feature and the minimum volume feature among the volume features of the problem;

    Calculate the average volume characteristics of all questions;

    Calculating a volume feature amplitude value between the maximum volume feature and the minimum volume feature;

    Determine the volume fluctuation range of each question according to the percentage of the absolute value of the difference between the volume characteristic of each question and the average volume characteristic of all the questions in the volume characteristic amplitude value;

    Determine the emotional stability of the interviewee based on the average of the volume fluctuations of all questions.
19. The computer-readable storage medium according to any one of claims 16 to 18, wherein the computer-readable instructions are executed by the processor to realize the use of a pre-built confidence determination model to control the speech rate The characteristics, intermittent duration and duration are judged, and when determining the confidence of the interviewer, it specifically includes:

    Use a pre-built confidence judgment model to judge the speech rate characteristics, interruption time and duration of each question, and determine the confidence level of each question;

    Convert the confidence level obtained from all questions into numerical values;

    Take the average of the confidence level data of all questions;

    The average is rounded up to get the interviewer’s confidence judgment result.
20. 19. The computer-readable storage medium of claim 19, wherein the computer-readable instructions are executed by the processor to implement the speech rate characteristics of each question using the pre-built confidence determination model, Intermittent time and continuous time are judged, and the confidence level of each question is determined, including:

    Using a pre-built confidence determination model to respectively identify the characteristic range corresponding to the confidence level to which the speech rate feature, the interruption duration, and the duration belong;

    Determining the confidence level corresponding to the characteristic range that belongs to the first confidence level of the speaking rate feature, the second confidence level of the intermittent duration, and the third confidence level of the continuous duration;

    Judging whether there are multiple levels of the first confidence level, the second confidence level, and the third confidence level;

    If there are multiple levels of the first confidence level, the second confidence level, and the third confidence level and they are all the same, determine the multiple same levels as the candidate confidence level;

    Sorting the multiple candidate confidence levels in descending order of confidence level to obtain a confidence level ranking queue;

    Based on the law of large numbers, it is determined that the confidence level of the target candidate in the confidence level ranking queue is the confidence level of the problem.
21. 20. The computer-readable storage medium of claim 20, wherein if the first confidence level, the second confidence level, and the third confidence level are multiple and are not uniformly the same, the computer readable instruction Execution by the processor also implements the following steps:

    Determine whether the multiple levels of the first confidence level, the second confidence level and the third confidence level have the same level;

    If there are the same grades, the same grades are determined as candidate confidence grades.
22. 22. The computer-readable storage medium of claim 20, wherein if the first confidence level, the second confidence level, and the third confidence level are one and the same, the computer readable instruction is The processor execution also implements the following steps:

    It is determined that the confidence level of the question is the neutral level.

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