CN115118820A - Call processing method and device, computer equipment and storage medium - Google Patents

Call processing method and device, computer equipment and storage medium Download PDF

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CN115118820A
CN115118820A CN202210731432.4A CN202210731432A CN115118820A CN 115118820 A CN115118820 A CN 115118820A CN 202210731432 A CN202210731432 A CN 202210731432A CN 115118820 A CN115118820 A CN 115118820A
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template
target user
call
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陈杭
陈子意
朱益兴
于欣璐
李骁
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Ping An Bank Co Ltd
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Ping An Bank Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04M—TELEPHONIC COMMUNICATION
    • H04M3/00—Automatic or semi-automatic exchanges
    • H04M3/42—Systems providing special services or facilities to subscribers
    • H04M3/42221—Conversation recording systems
    • G—PHYSICS
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    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/02—Feature extraction for speech recognition; Selection of recognition unit
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/08—Speech classification or search
    • G10L15/16—Speech classification or search using artificial neural networks
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/26—Speech to text systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04M—TELEPHONIC COMMUNICATION
    • H04M3/00—Automatic or semi-automatic exchanges
    • H04M3/42—Systems providing special services or facilities to subscribers
    • H04M3/487—Arrangements for providing information services, e.g. recorded voice services or time announcements
    • H04M3/493—Interactive information services, e.g. directory enquiries ; Arrangements therefor, e.g. interactive voice response [IVR] systems or voice portals
    • H04M3/4936—Speech interaction details
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04M—TELEPHONIC COMMUNICATION
    • H04M3/00—Automatic or semi-automatic exchanges
    • H04M3/42—Systems providing special services or facilities to subscribers
    • H04M3/50—Centralised arrangements for answering calls; Centralised arrangements for recording messages for absent or busy subscribers ; Centralised arrangements for recording messages
    • H04M3/51—Centralised call answering arrangements requiring operator intervention, e.g. call or contact centers for telemarketing
    • H04M3/5166—Centralised call answering arrangements requiring operator intervention, e.g. call or contact centers for telemarketing in combination with interactive voice response systems or voice portals, e.g. as front-ends

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Abstract

The embodiment of the application discloses a call processing method, a call processing device, computer equipment and a storage medium, wherein the scheme is used for acquiring a historical call record of a target user; respectively extracting the characteristics of the voice information and the text information corresponding to the historical call records based on a preset bimodal neural network model to obtain the voice characteristics and the text characteristics of the target user facing various types of call templates; determining the preference degree of the target user facing various types of speech templates according to the voice characteristics and the text characteristics of the target user facing various types of speech templates; based on the preference degree of the target user facing various types of speech templates, a first target speech template with the highest preference degree is determined from the various types of speech templates, and prompt operation is carried out on the first target speech template, so that a customer service seat can communicate with the target user according to the first target speech template, and the service quality of telephone seat service is improved.

Description

Call processing method and device, computer equipment and storage medium
Technical Field
The present application relates to the field of call technologies, and in particular, to a call processing method and apparatus, a computer device, and a storage medium.
Background
With the development of financial science and technology and social economy, banks specially set up telephone seat services to better serve customers. The existing telephone seat service mainly depends on the experience of a customer service seat to perform service, and the service quality of the telephone seat service is poor due to the fact that the mobility of the customer service seat is high and the professional levels are different.
Disclosure of Invention
The embodiment of the application provides a call processing method, a call processing device, computer equipment and a storage medium, which can improve the service quality of telephone seat service.
The embodiment of the application provides a call processing method, which comprises the following steps:
acquiring a historical call record of a target user, wherein the historical call record comprises a call record when the target user is called based on various types of call templates;
respectively extracting the characteristics of the voice information and the text information corresponding to the historical call records based on a preset bimodal neural network model to obtain the voice characteristics and the text characteristics of the target user facing various types of call templates;
determining the preference degree of the target user facing various types of voice templates according to the voice characteristics and the text characteristics of the target user facing various types of voice templates;
and determining a first target language template with the highest preference degree from the various types of language templates based on the preference degree of the target user facing the various types of language templates, and performing prompt operation on the first target language template so that the customer service seat can communicate with the target user according to the first target language template.
Correspondingly, an embodiment of the present application further provides a call processing apparatus, including:
the system comprises a record acquisition module, a storage module and a processing module, wherein the record acquisition module is used for acquiring a historical call record of a target user, and the historical call record comprises a call record when the target user is called based on various types of call templates;
the feature extraction module is used for respectively extracting features of the voice information and the text information corresponding to the historical call records based on a preset bimodal neural network model to obtain voice features and text features of the target user facing various types of call templates;
the degree determining module is used for determining the preference degree of the target user facing various types of speech templates according to the voice characteristics and the text characteristics of the target user facing various types of speech templates;
and the prompt operation module is used for determining a first target speech template with the highest preference degree from the various types of speech templates based on the preference degree of the target user facing the various types of speech templates, and performing prompt operation on the first target speech template so that the customer service seat can communicate with the target user according to the first target speech template.
In some embodiments, the call processing apparatus further includes:
the first information acquisition module is used for acquiring the attribute information of the target user when the target user is a new user;
the user determining module is used for determining historical users with similar attributes to the target user according to the attribute information of the target user;
and the template acquisition module is used for acquiring a second target speech template with the highest preference degree corresponding to the historical user and carrying out prompt operation on the second target speech template so that the customer service agent can communicate with the target user according to the second target speech template.
In some embodiments, the call processing apparatus further includes:
the second information acquisition module is used for acquiring the call information of the target user within a preset time period when the target user is a new user;
a scene determining module, configured to determine a service scene required by the target user according to the call information;
and the template determining module is used for determining a third target speech template corresponding to the service scene according to the service scene and performing prompt operation on the third target speech template so as to enable the customer service seat to communicate with the target user according to the third target speech template.
In some embodiments, the call processing apparatus further includes:
the prediction module is used for acquiring a call record in a call process in real time, inputting the call record into a preset neural network model for preference degree prediction to obtain the preference degree of the target user on the first target call template;
and the selecting module is used for selecting a fourth target speech technology template from a preset template set according to the call record when the preference degree is smaller than a preset threshold value, and performing prompt operation on the fourth target speech technology template so that the customer service seat can communicate with the target user according to the fourth target speech technology template.
In some embodiments, the selecting module includes:
a scene determining unit, configured to determine a service scene currently required by the target user according to the call record;
a first selecting unit, configured to select a fourth target speech template corresponding to the service scene from the template set.
In some embodiments, the first target speech template includes at least one service phase, the service phase corresponds to at least one speech technology sub-template, and the speech processing apparatus further includes:
the stage determining module is used for acquiring the current reply information of the target user and determining the current service stage according to the current reply information;
and the highlighting prompt module is used for selecting a target sub-template from at least one speech operation sub-template corresponding to the current business stage and carrying out highlighting prompt on the target sub-template.
In some embodiments, the highlighting cue module includes:
a state obtaining unit, configured to obtain an emotional state of the target user;
and the second selecting unit is used for selecting a target sub-template from at least one conversation sub-template corresponding to the current business stage according to the emotional state.
Accordingly, an embodiment of the present application further provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the call processing method provided in any of the embodiments of the present application.
Correspondingly, the embodiment of the application further provides a storage medium, wherein a plurality of instructions are stored in the storage medium, and the instructions are suitable for being loaded by the processor to execute the call processing method.
The method comprises the steps of obtaining a historical call record of a target user, wherein the historical call record comprises a call record when the target user is called based on various types of call templates; respectively extracting the characteristics of the voice information and the text information corresponding to the historical call records based on a preset bimodal neural network model to obtain the voice characteristics and the text characteristics of the target user facing various types of call templates; determining the preference degree of the target user facing various types of voice templates according to the voice characteristics and the text characteristics of the target user facing various types of voice templates; based on the preference degree of the target user facing various types of call operation templates, a first target call operation template with the highest preference degree is determined from the various types of call operation templates, prompt operation is carried out on the first target call operation template, so that a customer service seat can communicate with the target user according to the first target call operation template, the best call operation template of the target user is determined through the historical communication record of the target user, and then communication between the telephone service seat and the target user is promoted based on the call operation template, the professional level of the telephone service seat and the service quality of the telephone service are improved, and the communication experience of the user is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart illustrating a call processing method according to an embodiment of the present application.
Fig. 2 is a block diagram of a call processing apparatus according to an embodiment of the present application.
Fig. 3 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The embodiment of the application provides a call processing method, a call processing device, a storage medium and computer equipment. Specifically, the call processing method in the embodiment of the present application may be executed by a computer device, where the computer device may be a server, or may also be a terminal or other devices. The server can be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, and can also be a cloud server for providing basic cloud computing services such as cloud service, a cloud database, cloud computing, cloud functions, cloud storage, network service, cloud communication, middleware service, domain name service, security service, CDN (content delivery network), big data and artificial intelligence platforms and the like. The terminal may be, but is not limited to, a smart phone, a desktop computer, a notebook computer, a tablet computer, etc. The terminal and the server may be directly or indirectly connected through wired or wireless communication, and the application is not limited herein.
For example, the computer device may be a terminal, and the terminal may obtain a historical call record of a target user, where the historical call record includes call records when the target user is called based on various types of call templates; respectively extracting the characteristics of the voice information and the text information corresponding to the historical call records based on a preset bimodal neural network model to obtain the voice characteristics and the text characteristics of the target user facing various types of call templates; determining the preference degree of the target user facing various types of voice templates according to the voice characteristics and the text characteristics of the target user facing various types of voice templates; and determining a first target language template with the highest preference degree from the various types of language templates based on the preference degree of the target user facing the various types of language templates, and performing prompt operation on the first target language template so that the customer service seat can communicate with the target user according to the first target language template.
Based on the foregoing problems, embodiments of the present application provide a call processing method, an apparatus, a computer device, and a storage medium, which can improve the service quality of a telephone seat service.
The following are detailed below. It should be noted that the following description of the embodiments is not intended to limit the preferred order of the embodiments.
The embodiment of the present application provides a call processing method, which may be executed by a terminal or a server, and the call processing method is described as an example executed by the terminal in the embodiment of the present application.
Referring to fig. 1, fig. 1 is a schematic flowchart illustrating a call processing method according to an embodiment of the present disclosure. The specific flow of the call processing method can be as follows:
101. and acquiring the historical call record of the target user.
In this embodiment, the historical call record is a call record generated when the customer service agent makes a call to the target user based on various types of call templates in the historical time, and the historical call record includes a call record when the customer service agent makes a call to the target user based on various types of call templates.
Wherein, the customer service seat is a telephone customer service person who communicates with the user; the above-mentioned dialect template includes but is not limited to the related content of calling, the related content of replying to the user question, and the related content of asking the user to wait; the types of dialoging templates include, but are not limited to, a compact type, a gentle type, a lovely type, etc.
102. And respectively extracting the characteristics of the voice information and the text information corresponding to the historical call records based on a preset bimodal neural network model to obtain the voice characteristics and the text characteristics of the target user facing various types of call templates.
In this embodiment, pre-call voice data and text data are trained to obtain a bimodal neural network model, and when a business requirement exists, that is, when a current customer service seat needs to make a call with a target user, a terminal can extract features of two modes from an acquired historical call record of the target user through the bimodal neural network model, wherein decibels of the two modes are two modes of voice and text, so that voice features and text features of the target user facing each specific type of speech template are obtained through feature extraction of the bimodal neural network model.
It can be understood that, in general, the history call record includes customer service voice and user voice, and since feature extraction is performed currently mainly for the user voice, the user voice can be extracted from the history call record, so as to perform feature extraction on the user voice, and when feature extraction of a text is performed, the user voice can be converted into text information in advance, and then voice information corresponding to the user voice and the text information after the user voice conversion are input into a preset bimodal neural network model for feature extraction.
103. And determining the preference degree of the target user facing various types of the conversational templates according to the voice characteristics and the text characteristics of the target user facing various types of the conversational templates.
In this embodiment, since the target user has different preference degrees for different types of speaking templates, after obtaining the speech features and text features of the target user facing each specific type of speaking template, the preference degree of the target user facing each specific type of speaking template can be identified by performing preference degree identification on the extracted speech features and text features based on the bimodal neural network model.
For example, if the current history call record of the target user includes three types of utterance templates, which are respectively a simple type of utterance template, a mild type of utterance template, and a lovely type of utterance template, based on the bimodal neural network model, it can be obtained that the preference degree of the target user when facing the simple type of utterance template is a, the preference degree of the target user when facing the mild type of utterance template is B, and the preference degree of the target user when facing the lovely type of utterance template is C.
104. And determining a first target language operation template with the highest preference degree from the various types of language operation templates based on the preference degree of the target user facing the various types of language operation templates, and performing prompt operation on the first target language operation template so that the customer service seat can communicate with the target user according to the first target language operation template.
In this embodiment, a first target speech template, which is a speech template with a higher relative preference degree of a target user, is selected from various types of speech templates in a history call record of the target user, so that the first target speech template can be displayed on a touch display screen of a terminal, a customer service seat is prompted to communicate with the target user according to the first target speech template displayed on the display screen, and the service quality of the customer service seat is improved by communicating with the target user by using the speech template with the highest preference degree of the target user.
For example, based on the above example, it is further defined that B is greater than C and greater than a, so that a first target utterance template with a higher relative preference degree of a target user is a gentle type utterance template, and the terminal performs a prompt operation on the gentle type utterance template, so that the customer service seat communicates with the target user according to the gentle type utterance template.
In some embodiments, because different speech templates correspond to different service scenes, at least two speech templates of the same type respectively correspond to different service scenes, and the content of the speech templates for different service scenes is different, in order to better improve the service quality of the telephone seat service, after the prompt operation is performed on the first target speech template, the method can further include detecting the call records of the customer service seat and the target user in real time to judge whether the service scene required by the current target user is consistent with the service scene corresponding to the first speech template obtained before communication in real time, so that the service scenes of the customer service seat and the target user are timely matched when the service scenes of the customer service seat and the target user are inconsistent, and the service quality of the telephone seat service is improved. The coping manner when the service scenes are inconsistent includes, but is not limited to, replacing the first target language template, modifying the first target language template, and the like; the business scenario includes, but is not limited to, an after-sales scenario, a consultation scenario, a sales scenario, and the like.
The method specifically comprises the following steps: the method comprises the steps that a terminal obtains a call record of a customer service agent in a call process based on a first call technology template and a target user in real time, inputs the call record into a preset neural network model to predict preference degree in real time, so that the preference degree of the target user to the first target call technology template is obtained, and when the preference degree of the target user to the first target call technology template is larger than or equal to a preset threshold value, the target user still satisfies the first target call technology template at present, and the first target call technology template is not processed; and when the preference degree of the target user to the first target speech technology template is smaller than the preset threshold value, the target user is not satisfied with the first target speech technology template, and unfriendly behaviors such as customer complaints and the like are possible.
The type of the fourth target language template may be the same as that of the first target language template, and the service scenarios of the fourth target language template and the first target language template are different.
The selecting a fourth target speech template from the preset template set according to the call record may specifically include: the terminal determines the current required service scene of the target user according to the call record; and selecting a fourth target speech template corresponding to the service scene from the template set. The template set can be determined according to the type of the first target language template, namely, the language templates with the consistent types and the service scenes can be stored in the same template set, when a demand exists, the corresponding template set is determined according to the type of the first target language template, then a fourth target language template corresponding to the service scenes is selected from the determined template set, the type of the fourth target language template is consistent with that of the first target language template, and the service scenes of the fourth target language template are more consistent with the current demand of a target user.
In some embodiments, since different service scenarios have different phases, for example, a call-in phase, a user question reply phase, and a user waiting phase, etc., the first target language template applied to a specific service scenario also includes at least one service phase, and the service phase corresponds to at least one language sub-template, that is, a sub-template corresponding to the same type but different in language manner, and in order to guarantee the service quality, after performing the prompt operation on the first target language template, the method may further include: the terminal obtains current reply information of a target user, wherein the reply information is equivalent to the reply content of a speech of a previous stage of customer service answered by the user, so that which business stage of a business scene the communication is in at present can be determined according to the current reply information, namely the current business stage is determined.
The selecting a target sub-template from the at least one session sub-template corresponding to the current service phase may specifically include: and selecting a target sub-template from the at least one speech technology sub-template corresponding to the current service stage by adopting a random selection mode, or selecting the target sub-template from the at least one speech technology sub-template corresponding to the current service stage by adopting a specific rule selection mode. Wherein, the specific rule may be determined according to the current state or attribute information of the target user, and the current state may be an emotional state; the attribute information may be information of gender, age, occupation, etc. of the target user.
Specifically, selecting a target sub-template from at least one session sub-template corresponding to the current service phase according to the current state may include: the terminal can obtain the emotional state of the target user, and therefore the target sub-template is selected from at least one conversation sub-template corresponding to the current business stage according to the emotional state. The emotional state of the target user can be identified according to the call record by acquiring the call record of the customer service agent based on the first call template in the call process with the target user in real time.
In some embodiments, since there may be some target users that are new users and the target users have no history call records, so that when the target users are new users, the history call records of the target users cannot be obtained, and the first target speech template with the highest preference degree of the target users is determined according to the history call records, when the target users are new users, the speech template with the highest preference degree of the target users can be determined according to the attribute information of the target users or the call information of the target users within the preset time period. The attribute information may be information of gender, age, occupation, and the like of the target user.
It can be understood that, if a history exists according to a target user, but the first target speech template cannot be determined according to the history, that is, it is indicated that although the target user has the history speech record, the preference degrees of the target users corresponding to the speech records corresponding to different types of speech templates in the history speech record are too low, and thus the first target speech template cannot be obtained according to the speech template with the too low preference degree, the target user can be regarded as a new user.
The method specifically comprises the following steps: when the target user is a new user, the terminal acquires the attribute information of the target user to determine a historical user with similar attributes to the target user according to the attribute information of the target user, so that a second target language template with the highest preference degree corresponding to the historical user is acquired, and the second target language template is subjected to prompt operation, so that the customer service seat can communicate with the target user according to the second target language template.
The historical users include at least one, and the obtaining mode of the second target language template with the highest preference degree corresponding to the historical users can refer to the steps 101 to 104. When at least two historical users exist, selecting a language template with the highest preference degree from the language templates of the types respectively corresponding to the at least two historical users, namely a second target language template, according to the preference degrees of the language templates of the types respectively corresponding to the at least two historical users.
The method specifically comprises the following steps: when the target user is a new user, the terminal acquires the call information of the target user within a preset time period to determine a service scene required by the target user according to the call information, so that a third target call template corresponding to the service scene is determined according to the service scene, and the third target call template is subjected to prompt operation, so that the customer service seat can communicate with the target user according to the third target call template.
The service scene corresponds to at least one type of speech template, and a third target speech template can be selected from the at least one type of speech template in a random selection mode. And determining the target type speech template with the largest number of people as a third target speech template according to the number of people with preference degrees corresponding to the speech templates of various types larger than a preset degree threshold value.
In the embodiment of the invention, the first, second, third and fourth are only used as the distinguishing terminology template, and have no other special meanings.
The embodiment of the application discloses a call processing method, which comprises the following steps: acquiring a historical call record of a target user, wherein the historical call record comprises a call record when the target user is called based on various types of call templates; respectively extracting the characteristics of the voice information and the text information corresponding to the historical call records based on a preset bimodal neural network model to obtain the voice characteristics and the text characteristics of the target user facing various types of call templates; determining the preference degree of the target user facing various types of voice templates according to the voice characteristics and the text characteristics of the target user facing various types of voice templates; based on the preference degree of the target user facing various types of speech operation templates, determining a first target speech operation template with the highest preference degree from the various types of speech operation templates, and performing prompt operation on the first target speech operation template so that a customer service seat can communicate with the target user according to the first target speech operation template, thereby improving the service quality of telephone seat service.
In order to better implement the call processing method provided by the embodiment of the present application, the embodiment of the present application further provides a call processing apparatus based on the call processing method. The terms are the same as those in the above-mentioned call processing method, and specific implementation details may refer to the description in the method embodiment.
Referring to fig. 2, fig. 2 is a block diagram of a call processing apparatus according to an embodiment of the present disclosure, where the apparatus includes:
the record obtaining module 201 is configured to obtain a historical call record of the target user, where the historical call record includes a call record when the target user makes a call based on various types of call templates.
The feature extraction module 202 is configured to perform feature extraction on the voice information and the text information corresponding to the historical call record respectively based on a preset bimodal neural network model, so as to obtain voice features and text features of the target user when the target user faces various types of call templates.
And the degree determining module 203 is used for determining the preference degree of the target user facing various types of conversational templates according to the voice characteristic and the text characteristic of the target user facing various types of conversational templates.
And the prompt operation module 204 is configured to determine, based on the preference degree of the target user when facing the various types of speech templates, a first target speech template with the highest preference degree from the various types of speech templates, and perform prompt operation on the first target speech template, so that the customer service seat communicates with the target user according to the first target speech template.
In some embodiments, the call processing apparatus further includes:
and the first information acquisition module is used for acquiring the attribute information of the target user when the target user is a new user.
And the user determining module is used for determining the historical users with similar attributes to the target user according to the attribute information of the target user.
And the template acquisition module is used for acquiring a second target dialect template with the highest preference degree corresponding to the historical user and carrying out prompt operation on the second target dialect template so that the customer service seat can communicate with the target user according to the second target dialect template.
In some embodiments, the call processing apparatus further includes:
and the second information acquisition module is used for acquiring the call information of the target user within the preset time period when the target user is a new user.
And the scene determining module is used for determining the service scene required by the target user according to the call information.
And the template determining module is used for determining a third target speech template corresponding to the service scene according to the service scene and carrying out prompt operation on the third target speech template so that the customer service seat can communicate with the target user according to the third target speech template.
In some embodiments, the call processing apparatus further includes:
and the prediction module is used for acquiring the call record in the call process in real time, inputting the call record into a preset neural network model for preference degree prediction to obtain the preference degree of the target user on the first target call template.
And the selecting module is used for selecting a fourth target speech technology template from the preset template set according to the call record when the preference degree is smaller than the preset threshold value, and performing prompt operation on the fourth target speech technology template so that the customer service seat can communicate with the target user according to the fourth target speech technology template.
In some embodiments, the selecting module includes:
and the scene determining unit is used for determining the current required service scene of the target user according to the call record.
And the first selecting unit is used for selecting a fourth target speech template corresponding to the service scene from the template set.
In some embodiments, the first target speech template includes at least one service phase, and the service phase corresponds to at least one speech technology sub-template, and the speech processing apparatus further includes:
and the stage determining module is used for acquiring the current reply information of the target user and determining the current service stage according to the current reply information.
And the highlighting prompt module is used for selecting a target sub-template from at least one speech operation sub-template corresponding to the current business stage and carrying out highlighting prompt on the target sub-template.
In some embodiments, the highlighting cue module includes:
and the state acquisition unit is used for acquiring the emotional state of the target user.
And the second selection unit is used for selecting a target sub-template from at least one speech operation sub-template corresponding to the current business stage according to the emotional state.
The embodiment of the application discloses a call processing device, which is characterized in that a record acquisition module 201 is used for acquiring a historical call record of a target user, wherein the historical call record comprises a call record when the target user is called based on various types of call templates; the feature extraction module 202 is configured to perform feature extraction on the voice information and the text information corresponding to the historical call record based on a preset bimodal neural network model, so as to obtain voice features and text features of a target user when the target user faces various types of call templates; the degree determining module 203 is used for determining the preference degree of the target user facing various types of speaking templates according to the voice characteristics and the text characteristics of the target user facing various types of speaking templates; and the prompt operation module 204 is configured to determine, based on the preference degree of the target user when facing the various types of speech templates, a first target speech template with the highest preference degree from the various types of speech templates, and perform prompt operation on the first target speech template, so that the customer service seat communicates with the target user according to the first target speech template. Therefore, the service quality of the telephone seat service is improved.
Correspondingly, the embodiment of the application also provides a computer device, and the computer device can be a terminal. As shown in fig. 3, fig. 3 is a schematic structural diagram of a computer device according to an embodiment of the present application. The computer apparatus 300 includes a processor 301 having one or more processing cores, a memory 302 having one or more computer-readable storage media, and a computer program stored on the memory 302 and executable on the processor. The processor 301 is electrically connected to the memory 302. Those skilled in the art will appreciate that the computer device configurations illustrated in the figures are not meant to be limiting of computer devices and may include more or fewer components than those illustrated, or some components may be combined, or a different arrangement of components.
The processor 301 is a control center of the computer apparatus 300, connects various parts of the entire computer apparatus 300 by various interfaces and lines, performs various functions of the computer apparatus 300 and processes data by running or loading software programs and/or modules stored in the memory 302, and calling data stored in the memory 302, thereby integrally monitoring the computer apparatus 300.
In the embodiment of the present application, the processor 301 in the computer device 300 loads instructions corresponding to processes of one or more application programs into the memory 302, and the processor 301 executes the application programs stored in the memory 302 according to the following steps, so as to implement various functions:
acquiring a historical call record of a target user, wherein the historical call record comprises call records when the target user is called based on various types of call templates;
respectively extracting the characteristics of the voice information and the text information corresponding to the historical call records based on a preset bimodal neural network model to obtain the voice characteristics and the text characteristics of the target user facing various types of call templates;
determining the preference degree of the target user facing various types of speech templates according to the voice characteristics and the text characteristics of the target user facing various types of speech templates;
and determining a first target language operation template with the highest preference degree from the various types of language operation templates based on the preference degree of the target user facing the various types of language operation templates, and performing prompt operation on the first target language operation template so that the customer service seat can communicate with the target user according to the first target language operation template.
The above operations can be implemented in the foregoing embodiments, and are not described in detail herein.
Optionally, as shown in fig. 3, the computer device 300 further includes: a touch display 303, a radio frequency circuit 304, an audio circuit 305, an input unit 306, and a power source 307. The processor 301 is electrically connected to the touch display 303, the radio frequency circuit 304, the audio circuit 305, the input unit 306, and the power source 307. Those skilled in the art will appreciate that the computer device configuration illustrated in FIG. 3 does not constitute a limitation of computer devices, and may include more or fewer components than those illustrated, or some components may be combined, or a different arrangement of components.
The touch display screen 303 may be used for displaying a graphical user interface and receiving operation instructions generated by a user acting on the graphical user interface. The touch display screen 303 may include a display panel and a touch panel. The display panel may be used, among other things, to display messages entered by or provided to a user and various graphical user interfaces of the computer device, which may be composed of graphics, text, icons, video, and any combination thereof. Alternatively, the display panel may be configured in the form of a Liquid crystal display (LCD, Liquid crystal display client account l display client account y), an organic Light-Emitting Diode (OLED), or the like. The touch panel may be used to collect touch operations of a user on or near the touch panel (for example, operations of the user on or near the touch panel using any suitable object or accessory such as a finger, a stylus pen, and the like), and generate corresponding operation instructions, and the operation instructions execute corresponding programs. Alternatively, the touch panel may include two parts, a touch detection device and a touch controller. The touch detection device detects the touch direction of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives the touch message from the touch sensing device, converts the touch message into touch point coordinates, sends the touch point coordinates to the processor 301, and can receive and execute commands sent by the processor 301. The touch panel may overlay the display panel, and when the touch panel detects a touch operation thereon or nearby, the touch panel transmits the touch operation to the processor 301 to determine the type of the touch event, and then the processor 301 provides a corresponding visual output on the display panel according to the type of the touch event. In the embodiment of the present application, the touch panel and the display panel may be integrated into the touch display screen 303 to realize input and output functions. However, in some embodiments, the touch panel and the touch panel can be implemented as two separate components to perform the input and output functions. That is, the touch display screen 303 may also be used as a part of the input unit 306 to implement an input function. In the embodiment of the present application, the processor 301 executes a call processing program to display a call template on the touch display screen 303.
The rf circuit 304 may be used for transceiving rf signals to establish wireless communication with a network device or other computer device via wireless communication, and for transceiving signals with the network device or other computer device.
The audio circuit 305 may be used to provide an audio interface between the user and the computer device through speakers, microphones. The audio circuit 305 may transmit the electrical signal converted from the received audio data to a speaker, and convert the electrical signal into a sound signal for output; on the other hand, the microphone converts the collected sound signal into an electric signal, which is received by the audio circuit 305 and converted into audio data, which is then processed by the audio data output processor 301, and then transmitted to, for example, another computer device via the radio frequency circuit 304, or output to the memory 302 for further processing. The audio circuit 305 may also include an earbud jack to provide communication of a peripheral headset with the computer device.
The input unit 306 may be used to receive input numbers, character messages, or user characteristic messages (e.g., fingerprints, irises, facial messages, etc.), and to generate keyboard, mouse, joystick, optical, or trackball signal inputs related to user settings and function control.
The power supply 307 is used to power the various components of the computer device 300. Optionally, the power supply 307 may be logically connected to the processor 301 through a power management system, so as to implement functions of managing charging, discharging, and power consumption management through the power management system. Power supply 307 may also include any component of one or more dc or ac power sources, recharging systems, power failure detection circuitry, power converters or inverters, power status indicators, and the like.
Although not shown in fig. 3, the computer device 300 may further include a camera, a sensor, a wireless fidelity module, a bluetooth module, etc., which are not described in detail herein.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
As can be seen from the above, the computer device provided in this embodiment obtains the historical call records of the target user, where the historical call records include call records when the target user makes a call based on various types of call templates; respectively extracting the characteristics of the voice information and the text information corresponding to the historical call records based on a preset bimodal neural network model to obtain the voice characteristics and the text characteristics of the target user facing various types of call templates; determining the preference degree of the target user facing various types of speech templates according to the voice characteristics and the text characteristics of the target user facing various types of speech templates; and determining a first target language operation template with the highest preference degree from the various types of language operation templates based on the preference degree of the target user facing the various types of language operation templates, and performing prompt operation on the first target language operation template so that the customer service seat can communicate with the target user according to the first target language operation template.
It will be understood by those skilled in the art that all or part of the steps of the methods of the above embodiments may be performed by instructions or by associated hardware controlled by the instructions, which may be stored in a computer readable storage medium and loaded and executed by a processor.
To this end, embodiments of the present application provide a computer-readable storage medium, in which a plurality of computer programs are stored, where the computer programs can be loaded by a processor to execute the steps in any one of the call processing methods provided in the embodiments of the present application. For example, the computer program may perform the steps of:
acquiring a historical call record of a target user, wherein the historical call record comprises call records when the target user is called based on various types of call templates;
respectively extracting the characteristics of the voice information and the text information corresponding to the historical call records based on a preset bimodal neural network model to obtain the voice characteristics and the text characteristics of the target user facing various types of call templates;
determining the preference degree of the target user facing various types of speech templates according to the voice characteristics and the text characteristics of the target user facing various types of speech templates;
and determining a first target language operation template with the highest preference degree from the various types of language operation templates based on the preference degree of the target user facing the various types of language operation templates, and performing prompt operation on the first target language operation template so that the customer service seat can communicate with the target user according to the first target language operation template.
The above operations can be implemented in the foregoing embodiments, and are not described in detail herein.
Wherein the storage medium may include: a read Only Memory (ROM, Re client account d Only Memory), a random access Memory (R client account M, R client account and access Memory), a magnetic disk or an optical disk, and the like.
Since the computer program stored in the storage medium can execute the steps in any of the call processing methods provided in the embodiments of the present application, beneficial effects that can be achieved by any of the call processing methods provided in the embodiments of the present application can be achieved, which are detailed in the foregoing embodiments and will not be described herein again.
The above detailed description is given of a call processing method, a call processing apparatus, a storage medium, and a computer device provided in the embodiments of the present application, and a specific example is applied in the present application to explain the principle and the implementation of the present application, and the description of the above embodiments is only used to help understanding the method and the core idea of the present application; meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation manner and the application scope may be changed, and in summary, the content of the present specification should not be construed as a limitation to the present application.

Claims (10)

1. A call processing method, comprising:
acquiring a historical call record of a target user, wherein the historical call record comprises a call record when the target user is called based on various types of call templates;
respectively extracting the characteristics of the voice information and the text information corresponding to the historical call records based on a preset bimodal neural network model to obtain the voice characteristics and the text characteristics of the target user facing various types of call templates;
determining the preference degree of the target user facing various types of conversational templates according to the voice features and the text features of the target user facing various types of conversational templates;
and determining a first target speech technology template with the highest preference degree from the various types of speech technology templates based on the preference degree of the target user facing the various types of speech technology templates, and performing prompt operation on the first target speech technology template so that a customer service agent can communicate with the target user according to the first target speech technology template.
2. The method of claim 1, further comprising:
when the target user is a new user, acquiring attribute information of the target user;
determining historical users with similar attributes to the target user according to the attribute information of the target user;
and acquiring a second target dialect template with the highest preference degree corresponding to the historical user, and performing prompt operation on the second target dialect template so as to enable the customer service seat to communicate with the target user according to the second target dialect template.
3. The method of claim 1, further comprising:
when the target user is a new user, acquiring call information of the target user within a preset time period;
determining a service scene required by the target user according to the call information;
and determining a third target speech technology template corresponding to the service scene according to the service scene, and performing prompt operation on the third target speech technology template so as to enable the customer service agent to communicate with the target user according to the third target speech technology template.
4. The method of claim 1, further comprising, after prompting the first target surgery template:
acquiring a call record in a call process in real time, inputting the call record into a preset neural network model for preference degree prediction to obtain the preference degree of the target user on the first target call template;
and when the preference degree is smaller than a preset threshold value, selecting a fourth target speech template from a preset template set according to the call record, and performing prompt operation on the fourth target speech template so that the customer service seat communicates with the target user according to the fourth target speech template.
5. The method of claim 4, wherein selecting a fourth target session template from a preset set of templates according to the session record comprises:
determining a service scene currently required by the target user according to the call record;
and selecting a fourth target language template corresponding to the service scene from the template set.
6. The method of claim 1, wherein the first target speech template comprises at least one business phase corresponding to at least one speech sub-template, and further comprising, after performing a prompt operation on the first target speech template:
acquiring current reply information of a target user, and determining a current service stage according to the current reply information;
and selecting a target sub-template from at least one speech operation sub-template corresponding to the current business stage, and carrying out bright prompt on the target sub-template.
7. The method of claim 1, wherein the selecting a target sub-template from the at least one speech technology sub-template corresponding to the current business phase comprises:
acquiring the emotional state of the target user;
and selecting a target sub-template from at least one session sub-template corresponding to the current service stage according to the emotional state.
8. A call processing apparatus, comprising:
the system comprises a record acquisition module, a storage module and a processing module, wherein the record acquisition module is used for acquiring a historical call record of a target user, and the historical call record comprises a call record when the target user is called based on various types of call templates;
the feature extraction module is used for respectively extracting features of the voice information and the text information corresponding to the historical call records based on a preset bimodal neural network model to obtain voice features and text features of the target user facing various types of call templates;
the degree determining module is used for determining the preference degree of the target user facing various types of speaking templates according to the voice characteristics and the text characteristics of the target user facing various types of speaking templates;
and the prompt operation module is used for determining a first target language operation template with the highest preference degree from the various types of language operation templates based on the preference degree of the target user facing the various types of language operation templates, and performing prompt operation on the first target language operation template so that the customer service seat can communicate with the target user according to the first target language operation template.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and running on the processor, wherein the processor implements the call processing method according to any one of claims 1 to 7 when executing the program.
10. A storage medium storing instructions adapted to be loaded by a processor to perform the call processing method according to any one of claims 1 to 7.
CN202210731432.4A 2022-06-24 2022-06-24 Call processing method and device, computer equipment and storage medium Pending CN115118820A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116208712A (en) * 2023-05-04 2023-06-02 北京智齿众服技术咨询有限公司 Intelligent outbound method, system, equipment and medium for improving user intention

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116208712A (en) * 2023-05-04 2023-06-02 北京智齿众服技术咨询有限公司 Intelligent outbound method, system, equipment and medium for improving user intention

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