CN112836037A - Method and device for recommending language skills - Google Patents
Method and device for recommending language skills Download PDFInfo
- Publication number
- CN112836037A CN112836037A CN202110330720.4A CN202110330720A CN112836037A CN 112836037 A CN112836037 A CN 112836037A CN 202110330720 A CN202110330720 A CN 202110330720A CN 112836037 A CN112836037 A CN 112836037A
- Authority
- CN
- China
- Prior art keywords
- recommendation
- speech
- recording file
- collection
- list
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/332—Query formulation
- G06F16/3329—Natural language query formulation
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/02—Banking, e.g. interest calculation or account maintenance
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Mathematical Physics (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Technology Law (AREA)
- General Business, Economics & Management (AREA)
- Strategic Management (AREA)
- Marketing (AREA)
- Artificial Intelligence (AREA)
- Human Computer Interaction (AREA)
- Development Economics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- General Engineering & Computer Science (AREA)
- Telephonic Communication Services (AREA)
Abstract
The application provides a conversation recommendation method and device, and relates to the technical field of artificial intelligence, wherein the method comprises the following steps: acquiring a batch collection service information group, wherein each collection service information group comprises: calling and receiving call information and client repayment information; if a service information group for urging collection of the client payment information according to the date exists, marking the to-be-processed recording file corresponding to the call information for urging collection of the external call of the service information group as a forward to-be-processed recording file; and applying a preset dialect recommendation algorithm and the forward to-be-processed sound recording file to generate and output a forward dialect recommendation list. The method and the device can effectively provide speech operation assistance for the seat personnel, and further can improve the efficiency and the success rate of the business collection.
Description
Technical Field
The application relates to the technical field of artificial intelligence, can also be used in the financial field, and particularly relates to a conversation recommendation method and device.
Background
With the continuous development of the financial field, the dialect skills play an important role in facilitating the payment of the client in the outbound collection service. Because the ability of the outside-call seat personnel is uneven and the mobility is larger, the outside-call seat personnel only carry out outside-call collection according to the communication experience, and the problems of low collection efficiency and low collection success rate are easy to exist.
Disclosure of Invention
Aiming at the problems in the prior art, the application provides a talk operation recommendation method and device, which can effectively provide talk operation assistance for seat personnel and further can improve the efficiency and success rate of the service of receiving and urging.
In order to solve the technical problem, the present application provides the following technical solutions:
in a first aspect, the present application provides a conversational recommendation method, including:
acquiring a batch collection service information group, wherein each collection service information group comprises: calling and receiving call information and client repayment information;
if a service information group for urging collection of the client payment information according to the date exists, marking the to-be-processed recording file corresponding to the call information for urging collection of the external call of the service information group as a forward to-be-processed recording file;
and applying a preset dialect recommendation algorithm and the forward to-be-processed sound recording file to generate and output a forward dialect recommendation list.
Further, the applying a preset dialect recommendation algorithm and the forward to-be-processed sound recording file to generate and output a forward dialect recommendation list includes:
converting the forward to-be-processed sound recording file into a conversation list between a forward position and a client;
applying the conversation list of the forward position and the client to obtain a forward keyword word and sentence set;
and applying a forward keyword word and sentence set and a preset speech recommendation algorithm to generate and output a forward speech recommendation list.
Further, each collection-hastening service information group further comprises: customer complaint information;
correspondingly, after the acquiring the batch service information group, the method further includes:
if a customer complaint information is a complaint information collection service information group, marking a to-be-processed recording file corresponding to the call information of the call outside the complaint information collection service information group as a negative to-be-processed recording file;
and applying a preset dialect recommendation algorithm and the positive to-be-processed sound recording file to generate and output a negative dialect recommendation list.
Further, the applying a preset dialect recommendation algorithm and the positive direction sound recording file to be processed to generate and output a negative dialect recommendation list includes:
converting the negative direction sound recording file to be processed into a negative direction seat and client dialogue list;
applying the negative direction seat and a dialogue list of the client to obtain a negative direction keyword word and sentence set;
and generating and outputting a negative-direction speech recommendation list by applying a negative-direction keyword word and sentence set and a preset speech recommendation algorithm.
Further, the preset conversational recommendation algorithm is one of a correlation analysis algorithm, a frequency algorithm and a classification algorithm.
Further, the applying the dialog list of the forward position and the client to obtain a forward keyword word and sentence set includes:
obtaining a forward position statement from a conversation list of the forward position and a client;
extracting forward keyword words from the forward position sentences;
each forward keyword word and sentence constitutes the forward keyword word and sentence set.
Further, the generating and outputting a forward speech recommendation list by applying the forward keyword word and sentence set and a preset speech recommendation algorithm includes:
and generating and outputting a forward speech recommendation list by applying a forward keyword word and sentence set and a preset speech recommendation model, wherein the preset speech recommendation model is obtained by applying XGBOST algorithm pre-training.
In a second aspect, the present application provides a tactical recommendation apparatus, comprising:
the acquisition module is used for acquiring the batch collection service information groups, and each group of collection service information groups comprises: calling and receiving call information and client repayment information;
the marking module is used for marking the to-be-processed recording file corresponding to the outbound payment-prompting call information of the payment-prompting service information group as a forward to-be-processed recording file if the payment-prompting service information group of which the client payment information is the on-date payment information exists;
and the generating module is used for applying a preset dialect recommendation algorithm and the forward to-be-processed sound recording file, generating and outputting a forward dialect recommendation list.
In a third aspect, the present application provides an electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the method for recommending dialogies when executing the program.
In a fourth aspect, the present application provides a computer-readable storage medium having stored thereon computer instructions that, when executed, implement the conversational recommendation method.
According to the technical scheme, the application provides a conversation recommendation method and device. Wherein, the method comprises the following steps: acquiring a batch collection service information group, wherein each collection service information group comprises: calling and receiving call information and client repayment information; if a service information group for urging collection of the client payment information according to the date exists, marking the to-be-processed recording file corresponding to the call information for urging collection of the external call of the service information group as a forward to-be-processed recording file; a preset speech recommendation algorithm and the forward to-be-processed recording file are applied to generate and output a forward speech recommendation list, so that speech assistance can be effectively provided for seat personnel, and the efficiency and the success rate of the service of collection can be improved; specifically, the accumulation capacity of forward and reverse speech knowledge can be improved, the service training efficiency, the service developing efficiency and the service capacity of the person calling out the seat can be improved, the cost is saved, the communication level with the client can be improved, and the enterprise image is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a conversational recommendation method in an embodiment of the application;
FIG. 2 is a flowchart illustrating steps 201 to 203 of a conversational recommendation method in an embodiment of the application;
FIG. 3 is a flow chart illustrating a method for conversational recommendation in another embodiment of the present application;
FIG. 4 is a schematic structural diagram of a speech recommendation device in an embodiment of the present application;
FIG. 5 is a schematic diagram of a speech recommendation device in an application example of the present application;
fig. 6 is a schematic block diagram of a system configuration of an electronic device 9600 according to an embodiment of the present application.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present application will be clearly and completely described below 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.
Based on this, in order to effectively provide the agent with the speech assistance and further improve the efficiency and the success rate of the hastening service, the embodiment of the present application provides a speech recommendation device, which may be a server or a client device, where the client device may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a Personal Digital Assistant (PDA), a vehicle-mounted device, an intelligent wearable device, and the like. Wherein, intelligence wearing equipment can include intelligent glasses, intelligent wrist-watch and intelligent bracelet etc..
In practical applications, the part for making the conversational recommendation may be performed on the server side as described in the above, or all operations may be performed in the client device. The selection may be specifically performed according to the processing capability of the client device, the limitation of the user usage scenario, and the like. This is not a limitation of the present application. The client device may further include a processor if all operations are performed in the client device.
The client device may have a communication module (i.e., a communication unit), and may be communicatively connected to a remote server to implement data transmission with the server. The server may include a server on the task scheduling center side, and in other implementation scenarios, the server may also include a server on an intermediate platform, for example, a server on a third-party server platform that is communicatively linked to the task scheduling center server. The server may include a single computer device, or may include a server cluster formed by a plurality of servers, or a server structure of a distributed apparatus.
The server and the client device may communicate using any suitable network protocol, including network protocols not yet developed at the filing date of this application. The network protocol may include, for example, a TCP/IP protocol, a UDP/IP protocol, an HTTP protocol, an HTTPS protocol, or the like. Of course, the network Protocol may also include, for example, an RPC Protocol (Remote Procedure Call Protocol), a REST Protocol (Representational State Transfer Protocol), and the like used above the above Protocol.
It should be noted that the method and apparatus for recommending dialogies disclosed in the present application can be used in the field of financial technology, and can also be used in any field other than the field of financial technology.
The following examples are intended to illustrate the details.
In order to effectively provide the agent with the speech aid and further improve the efficiency and success rate of the service of receiving the call, the embodiment provides a speech recommendation method in which the execution subject is a speech recommendation device, the speech recommendation device includes but is not limited to a server, as shown in fig. 1, the method specifically includes the following contents:
step 101: acquiring a batch collection service information group, wherein each collection service information group comprises: the outbound call is used for receiving call information and client payment information.
Specifically, a batch service information group for urging to receive can be obtained at regular time; the outbound call reception call information may include: outbound time, outbound seat number, outbound customer information, etc.; the client payment information is used for indicating whether the client pays within a preset time after being hasten paid, and the client payment information may include: the information of payment according to the date or the information of payment not according to the date; in one example, if the client payment information is "1", it indicates a payment by date, and if the client payment information is "0", it indicates that the payment by date is not made; each group of the service information collection prompting groups can respectively correspond to one external call prompting.
Step 102: and if the customer payment information is a collection urging service information group of the on-schedule payment information, marking the to-be-processed recording file corresponding to the call information of the call urging collection outside the collection urging service information group as a forward to-be-processed recording file.
Specifically, the to-be-processed sound recording file corresponding to the collection-urging service information group may be locally obtained from the call technology recommendation apparatus.
Step 103: and applying a preset dialect recommendation algorithm and the forward to-be-processed sound recording file to generate and output a forward dialect recommendation list.
It is understood that the forward dialoging recommendation list is a list of dialoging that suggests the out-call agent to use in the call; the forward language recommendation list may be output to a terminal device of an out-call agent.
To further improve the efficiency and success rate of the harvest, referring to fig. 2, in an embodiment of the present application, step 103 includes:
step 201: and converting the forward to-be-processed sound recording file into a conversation list between a forward position and a client.
Specifically, an Automatic Speech Recognition (ASR) technique may be adopted to perform processing of converting a recording to a text for a recording file to be processed, and convert each recording file to be processed into a dialog list between an agent and a client, so as to separate an agent sentence from a client sentence, where the dialog list between the agent and the client includes: agent statements and customer statements.
Step 202: and applying the conversation list of the forward position and the client to obtain a forward keyword word and sentence set.
Specifically, in order to improve the reliability of the forward keyword word and sentence set, a forward position sentence can be obtained from a conversation list of the forward position and the client; a Natural Language Processing (NLP) technology can be applied to extract forward keyword sentences from the forward seat sentences; each forward keyword word and sentence constitutes the forward keyword word and sentence set.
Step 203: and applying a forward keyword word and sentence set and a preset speech recommendation algorithm to generate and output a forward speech recommendation list.
Specifically, the preset conversational recommendation algorithm may be one of a correlation analysis algorithm, a frequency algorithm, and a classification algorithm; for example, if the preset speech recommendation algorithm is a frequency algorithm, forward keyword words with an occurrence frequency greater than a frequency screening threshold may be extracted from the forward keyword word set, and the forward keyword words may be grouped into a forward speech recommendation list.
To further improve the accuracy and intelligence of generating the forward-speaking recommendation list, in one embodiment of the present application, step 203 comprises:
and generating and outputting a forward speech recommendation list by applying a forward keyword word and sentence set and a preset speech recommendation model, wherein the preset speech recommendation model is obtained by applying XGBOST algorithm pre-training.
In order to avoid dissatisfaction of customers caused in the external call account prompting process and further improve the efficiency and success rate of the prompt receipt, in an embodiment of the application, each group of the prompt receipt service information group further comprises: customer complaint information; correspondingly, referring to fig. 3, after step 101, the method further includes:
step 301: if the customer complaint information is a complaint information-urging service information group, marking the to-be-processed recording file corresponding to the call information of the call outside the complaint information-urging service information group as a negative to-be-processed recording file.
Specifically, the customer complaint information is used to indicate whether the customer is willing to accept a complaint about the outgoing call after being urged to be accepted, and the customer complaint information may include: complaint information or non-complaint information; in one example, if the customer complaint information is "1", it indicates a complaint, and if the customer complaint information is "0", it indicates no complaint.
It can be understood that, if there is a collection urging service information group in which the customer repayment information is the on-schedule repayment information and the customer complaint information is the complaint information, the to-be-processed sound recording files corresponding to the outgoing call collection urging call information of the collection urging service information group can be simultaneously marked as a positive to-be-processed sound recording file and a negative to-be-processed sound recording file, and both are used for generating a positive speech recommendation list and a negative speech recommendation list; if the customer repayment information is the non-due repayment information and the customer complaint information is the non-complaint information collection service information group, the to-be-processed recording file corresponding to the call information collected by the call collection service information group can be used as an invalid recording file, and the next processing is not carried out.
Step 302: and applying a preset dialect recommendation algorithm and the negative direction sound recording file to be processed to generate and output a negative direction dialect recommendation list.
It can be understood that the negative conversational recommendation list is a conversational list that suggests the out-call agent to avoid using during a conversation; the negative dialect recommendation list can be output to a terminal device of an out-call agent.
To further improve the efficiency and success rate of the harvest, in one embodiment of the present application, step 302 includes:
step 401: and converting the negative direction sound recording file to be processed into a negative direction dialogue list between the negative direction seat and the client.
Specifically, an automatic speech recognition technique may be applied to convert the negative-going pending audio file into a negative-going agent-to-client dialog list.
Step 402: and applying the negative direction seat and a dialogue list of the client to obtain a negative direction keyword word and sentence set.
Specifically, a negative direction agent statement may be obtained from a dialog list of the negative direction agent and the client; a natural language processing technology can be applied to extract negative keyword words and sentences from the negative position sentences; and each negative keyword word and sentence forms the negative keyword word and sentence set.
Step 403: and generating and outputting a negative-direction speech recommendation list by applying a negative-direction keyword word and sentence set and a preset speech recommendation algorithm.
For example, if the preset conversational recommendation algorithm is a frequency algorithm, negative keyword words with a frequency greater than a frequency screening threshold may be extracted from the negative keyword word set, and the negative keyword words may be grouped into a negative conversational recommendation list.
In terms of software, in order to effectively provide the agent with the speech aid and further improve the efficiency and the success rate of the call-receiving service, the present application provides an embodiment of a speech recommendation device for implementing all or part of the content in the speech recommendation method, referring to fig. 4, where the speech recommendation device specifically includes the following content:
the acquiring module 10 is configured to acquire a batch collection service information group, where each collection service information group includes: the outbound call is used for receiving call information and client payment information.
And the marking module 20 is configured to mark the to-be-processed sound recording file corresponding to the outbound payment-prompting call information of the payment-prompting service information group as a forward to-be-processed sound recording file if the payment-prompting service information group exists in which the client payment information is the on-date payment information.
And the generating module 30 is configured to apply a preset conversational recommendation algorithm and the forward to-be-processed sound recording file to generate and output a forward conversational recommendation list.
The embodiment of the speech technology recommendation apparatus provided in this specification may be specifically configured to execute the processing procedure of the embodiment of the speech technology recommendation method, and the functions of the embodiment of the speech technology recommendation apparatus are not described herein again, and refer to the detailed description of the embodiment of the speech technology recommendation method.
To further illustrate the present solution, the present application provides an application example of a conversational recommendation method, referring to fig. 5, the apparatus includes: the system comprises a call information acquisition module 11, a customer complaint information acquisition module 12, a customer repayment information collection module 13, a call marking module 14, a call recording acquisition module 15, a recording file-to-text module 16, a statement analysis module 17 and a data analysis module 18; the function realized by the combination of the call information acquisition module 11, the customer complaint information acquisition module 12 and the customer payment information collection module 13 can be equivalent to the function realized by the acquisition module 10, the function realized by the combination of the call marking module 14 and the call recording acquisition module 15 can be equivalent to the function realized by the marking module 20, and the function realized by the combination of the recording file-to-text module 16, the sentence analysis module 17 and the data analysis module 18 can be equivalent to the function realized by the generation module 30; the technical recommendation device is described in detail as follows:
the call information obtaining module 11 is configured to collect call information of the outbound call and obtain a development situation of the service of the outbound call, and includes: outbound time, outbound seat number, outbound customer information, etc.
The customer complaint information acquisition module 12 is configured to collect complaint records of customers for outbound call solicitation services, and includes: customer information, complained agent numbers, and the like.
And the client payment information collection module 13 is used for collecting the payment condition of the client within 7 days after the payment prompting action occurs to the client who prompts the payment through the outbound call.
And the call marking module 14 is used for marking the associated outbound call according to the customer complaint record and the customer repayment action, and marking the call as a positive call and a negative call.
And the call record acquisition module 15 is used for analyzing the marked outbound call and call collection, tracing back a call record file corresponding to the call to form a list of the record files to be processed, and inheriting the output of the call marking module to form positive and negative marks of the record files.
The recording file-to-text module 16 performs recording-to-text processing on the recording file to be processed by using an automatic speech recognition technology (ASR), converts each call recording into a dialog list between the seat and the client, and realizes the separation of the seat sentences and the client sentences.
The sentence analysis module 17 is used for processing the seat sentences in each call recording by adopting a Natural Language Processing (NLP) technology and extracting keyword words and sentences; and combining the positive marks and the negative marks of the recording file to form a positive word and sentence set and a negative word and sentence set.
And the data analysis module 18 is used for analyzing the positive word and sentence set and the negative word and sentence set respectively to form a positive and negative jargon recommendation list.
To further illustrate the present application, in combination with the above-mentioned speech recommendation apparatus, the present application provides an application example of a speech recommendation method, including:
acquiring call record information of an outbound call reception service; acquiring customer complaint records caused by external call receiving and customer repayment condition information after the action of the external call receiving occurs according to the customer information in the call record information; forming positive and negative call marks according to the customer complaint records and the customer payment condition information; acquiring a sound recording file and marking the sound recording file; converting the recorded sound into a text and separating a seat sentence and a client sentence; analyzing the seat sentences and extracting keyword words and sentences to form a set; positive and negative conversational recommendation lists are analyzed and formed.
According to the above description, the method and the device for recommending the dialect can effectively provide the dialect assistance for the seat personnel, so that the efficiency and the success rate of the service of hastening and receiving can be improved; specifically, the accumulation capacity of forward and reverse speech knowledge can be improved, the service training efficiency, the service developing efficiency and the service capacity of the person calling out the seat can be improved, the cost is saved, the communication level with the client can be improved, and the enterprise image is improved.
In terms of hardware, in order to effectively provide a speech aid for an agent and further improve the efficiency and success rate of a service of receiving a call, the present application provides an embodiment of an electronic device for implementing all or part of the content in the speech recommendation method, where the electronic device specifically includes the following content:
a processor (processor), a memory (memory), a communication Interface (Communications Interface), and a bus; the processor, the memory and the communication interface complete mutual communication through the bus; the communication interface is used for realizing information transmission between the speaking operation recommending device and the user terminal and other related equipment; the electronic device may be a desktop computer, a tablet computer, a mobile terminal, and the like, but the embodiment is not limited thereto. In this embodiment, the electronic device may be implemented with reference to the embodiment for implementing the speech technology recommendation method and the embodiment for implementing the speech technology recommendation apparatus in the embodiments, and the contents thereof are incorporated herein, and repeated details are not repeated herein.
Fig. 6 is a schematic block diagram of a system configuration of an electronic device 9600 according to an embodiment of the present application. As shown in fig. 6, the electronic device 9600 can include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. Notably, this FIG. 6 is exemplary; other types of structures may also be used in addition to or in place of the structure to implement telecommunications or other functions.
In one or more embodiments of the present application, the verbal recommendation function can be integrated into the central processor 9100. The central processor 9100 may be configured to control as follows:
step 101: acquiring a batch collection service information group, wherein each collection service information group comprises: the outbound call is used for receiving call information and client payment information.
Step 102: and if the customer payment information is a collection urging service information group of the on-schedule payment information, marking the to-be-processed recording file corresponding to the call information of the call urging collection outside the collection urging service information group as a forward to-be-processed recording file.
Step 103: and applying a preset dialect recommendation algorithm and the forward to-be-processed sound recording file to generate and output a forward dialect recommendation list.
From the above description, the electronic device provided in the embodiments of the present application can effectively provide the agent with speech assistance, so as to improve the efficiency and success rate of the hastening service.
In another embodiment, the dialect recommending device can be configured separately from the central processor 9100, for example, the dialect recommending device can be configured as a chip connected with the central processor 9100, and the dialect recommending function can be realized through the control of the central processor.
As shown in fig. 6, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is noted that the electronic device 9600 also does not necessarily include all of the components shown in fig. 6; further, the electronic device 9600 may further include components not shown in fig. 6, which may be referred to in the art.
As shown in fig. 6, a central processor 9100, sometimes referred to as a controller or operational control, can include a microprocessor or other processor device and/or logic device, which central processor 9100 receives input and controls the operation of the various components of the electronic device 9600.
The memory 9140 can be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information relating to the failure may be stored, and a program for executing the information may be stored. And the central processing unit 9100 can execute the program stored in the memory 9140 to realize information storage or processing, or the like.
The input unit 9120 provides input to the central processor 9100. The input unit 9120 is, for example, a key or a touch input device. Power supply 9170 is used to provide power to electronic device 9600. The display 9160 is used for displaying display objects such as images and characters. The display may be, for example, an LCD display, but is not limited thereto.
The memory 9140 can be a solid state memory, e.g., Read Only Memory (ROM), Random Access Memory (RAM), a SIM card, or the like. There may also be a memory that holds information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes called an EPROM or the like. The memory 9140 could also be some other type of device. Memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application/function storage portion 9142, the application/function storage portion 9142 being used for storing application programs and function programs or for executing a flow of operations of the electronic device 9600 by the central processor 9100.
The memory 9140 can also include a data store 9143, the data store 9143 being used to store data, such as contacts, digital data, pictures, sounds, and/or any other data used by an electronic device. The driver storage portion 9144 of the memory 9140 may include various drivers for the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging applications, contact book applications, etc.).
The communication module 9110 is a transmitter/receiver 9110 that transmits and receives signals via an antenna 9111. The communication module (transmitter/receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as in the case of a conventional mobile communication terminal.
Based on different communication technologies, a plurality of communication modules 9110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, may be provided in the same electronic device. The communication module (transmitter/receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby implementing ordinary telecommunications functions. The audio processor 9130 may include any suitable buffers, decoders, amplifiers and so forth. In addition, the audio processor 9130 is also coupled to the central processor 9100, thereby enabling recording locally through the microphone 9132 and enabling locally stored sounds to be played through the speaker 9131.
As can be seen from the above description, the electronic device provided in the embodiments of the present application can effectively provide the seat staff with speech assistance, so as to improve the efficiency and success rate of the call accepting service.
Embodiments of the present application further provide a computer-readable storage medium capable of implementing all steps in the dialog recommendation method in the above embodiments, where the computer-readable storage medium stores a computer program, and the computer program implements all steps of the dialog recommendation method in the above embodiments when executed by a processor, for example, the processor implements the following steps when executing the computer program:
step 101: acquiring a batch collection service information group, wherein each collection service information group comprises: the outbound call is used for receiving call information and client payment information.
Step 102: and if the customer payment information is a collection urging service information group of the on-schedule payment information, marking the to-be-processed recording file corresponding to the call information of the call urging collection outside the collection urging service information group as a forward to-be-processed recording file.
Step 103: and applying a preset dialect recommendation algorithm and the forward to-be-processed sound recording file to generate and output a forward dialect recommendation list.
As can be seen from the above description, the computer-readable storage medium provided in the embodiments of the present application can effectively provide a talk assisting function for an attendant, so as to improve the efficiency and success rate of the call accepting service.
In the present application, each embodiment of the method is described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. Reference is made to the description of the method embodiments.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principle and the implementation mode of the present application are explained by applying specific embodiments in 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 a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110330720.4A CN112836037B (en) | 2021-03-26 | 2021-03-26 | Method and device for recommending speech skills |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110330720.4A CN112836037B (en) | 2021-03-26 | 2021-03-26 | Method and device for recommending speech skills |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112836037A true CN112836037A (en) | 2021-05-25 |
| CN112836037B CN112836037B (en) | 2024-12-06 |
Family
ID=75930590
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110330720.4A Active CN112836037B (en) | 2021-03-26 | 2021-03-26 | Method and device for recommending speech skills |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112836037B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113705248A (en) * | 2021-07-21 | 2021-11-26 | 上海原圈网络科技有限公司 | Method and device for processing tactical training data based on result evaluation |
| CN113824828A (en) * | 2021-10-29 | 2021-12-21 | 平安普惠企业管理有限公司 | Dialing method and device, electronic equipment and computer readable storage medium |
| CN115905502A (en) * | 2022-07-19 | 2023-04-04 | 北京中关村科金技术有限公司 | Method, device and storage medium for mining and recommending dialect |
| CN116132592A (en) * | 2022-08-05 | 2023-05-16 | 马上消费金融股份有限公司 | Automatic outbound configuration method and device |
| CN116939110A (en) * | 2023-08-11 | 2023-10-24 | 北京灵伴即时智能科技有限公司 | Artificial agent assistance system and method |
| CN117978920A (en) * | 2024-02-01 | 2024-05-03 | 海尔消费金融有限公司 | Real-time prompting system and method for prompting conversation of prompting seat |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109064315A (en) * | 2018-08-02 | 2018-12-21 | 平安科技（深圳）有限公司 | Overdue bill intelligence collection method, apparatus, computer equipment and storage medium |
| CN109670166A (en) * | 2018-09-26 | 2019-04-23 | 平安科技（深圳）有限公司 | Collection householder method, device, equipment and storage medium based on speech recognition |
| CN109885679A (en) * | 2019-01-11 | 2019-06-14 | 平安科技（深圳）有限公司 | Method, apparatus, computer equipment and storage medium for obtaining preferred speech |
| CN110728145A (en) * | 2019-10-11 | 2020-01-24 | 集奥聚合(北京)人工智能科技有限公司 | Method for establishing natural language understanding model based on recording conversation |
| CN111259132A (en) * | 2020-01-16 | 2020-06-09 | 中国平安财产保险股份有限公司 | Method, apparatus, computer equipment and storage medium for speech recommendation |
-
2021
- 2021-03-26 CN CN202110330720.4A patent/CN112836037B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109064315A (en) * | 2018-08-02 | 2018-12-21 | 平安科技（深圳）有限公司 | Overdue bill intelligence collection method, apparatus, computer equipment and storage medium |
| CN109670166A (en) * | 2018-09-26 | 2019-04-23 | 平安科技（深圳）有限公司 | Collection householder method, device, equipment and storage medium based on speech recognition |
| CN109885679A (en) * | 2019-01-11 | 2019-06-14 | 平安科技（深圳）有限公司 | Method, apparatus, computer equipment and storage medium for obtaining preferred speech |
| CN110728145A (en) * | 2019-10-11 | 2020-01-24 | 集奥聚合(北京)人工智能科技有限公司 | Method for establishing natural language understanding model based on recording conversation |
| CN111259132A (en) * | 2020-01-16 | 2020-06-09 | 中国平安财产保险股份有限公司 | Method, apparatus, computer equipment and storage medium for speech recommendation |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113705248A (en) * | 2021-07-21 | 2021-11-26 | 上海原圈网络科技有限公司 | Method and device for processing tactical training data based on result evaluation |
| CN113824828A (en) * | 2021-10-29 | 2021-12-21 | 平安普惠企业管理有限公司 | Dialing method and device, electronic equipment and computer readable storage medium |
| CN113824828B (en) * | 2021-10-29 | 2024-03-08 | 河北科燃信息科技股份有限公司 | Dialing method and device, electronic equipment and computer readable storage medium |
| CN115905502A (en) * | 2022-07-19 | 2023-04-04 | 北京中关村科金技术有限公司 | Method, device and storage medium for mining and recommending dialect |
| CN115905502B (en) * | 2022-07-19 | 2024-01-05 | 北京中关村科金技术有限公司 | Speaking skill mining and recommending method, device and storage medium |
| CN116132592A (en) * | 2022-08-05 | 2023-05-16 | 马上消费金融股份有限公司 | Automatic outbound configuration method and device |
| CN116132592B (en) * | 2022-08-05 | 2025-11-25 | 马上消费金融股份有限公司 | Automatic outbound calling configuration method and device |
| CN116939110A (en) * | 2023-08-11 | 2023-10-24 | 北京灵伴即时智能科技有限公司 | Artificial agent assistance system and method |
| CN117978920A (en) * | 2024-02-01 | 2024-05-03 | 海尔消费金融有限公司 | Real-time prompting system and method for prompting conversation of prompting seat |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112836037B (en) | 2024-12-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112836037A (en) | Method and device for recommending language skills | |
| KR102381214B1 (en) | Man-machine dialogue method, device and electronic equipment | |
| CN107623614B (en) | Method and device for pushing information | |
| US10276153B2 (en) | Online chat communication analysis via mono-recording system and methods | |
| US8934652B2 (en) | Visual presentation of speaker-related information | |
| US20170316775A1 (en) | Dialog device with dialog support generated using a mixture of language models combined using a recurrent neural network | |
| US10412223B2 (en) | Personalized support routing based on paralinguistic information | |
| US8811638B2 (en) | Audible assistance | |
| US20130144595A1 (en) | Language translation based on speaker-related information | |
| CN112860877B (en) | Customer service operation processing method and device, electronic equipment and storage medium | |
| US20100217591A1 (en) | Vowel recognition system and method in speech to text applictions | |
| CN113257218A (en) | Speech synthesis method, speech synthesis device, electronic equipment and storage medium | |
| CN113782022B (en) | Communication method, device, equipment and storage medium based on intention recognition model | |
| KR102365611B1 (en) | Meeting management system using automatic speech recognition(ASR) | |
| CN113035188A (en) | Call text generation method, device, equipment and storage medium | |
| US10846429B2 (en) | Automated obscuring system and method | |
| US20250218440A1 (en) | Context-based speech assistance | |
| US20250078574A1 (en) | Automatic sign language interpreting | |
| CN111414748A (en) | Traffic data processing method and device | |
| CN118366456A (en) | Multi-person conversation voice transcription method, device, equipment, medium and program product | |
| CN113206996B (en) | Quality inspection method and device for service recorded data | |
| US20180255181A1 (en) | Utilizing caller wait time to create social message | |
| CN115098630A (en) | Supply and demand data generation method and device for cross-border matching service system negotiation | |
| CN115564481A (en) | Customer comment information analysis method and device | |
| CN113744712A (en) | Intelligent outbound voice splicing method, device, equipment, medium and program product |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |