Customer service call quality control method and system based on voice recognition

Abstract

The embodiment of the invention relates to the technical field of information, in particular to a customer service call quality control method and system based on voice recognition. The method comprises the following steps: identifying the voice as customer voice and customer service voice; identifying a call topic; obtaining a plurality of selectable answers, and respectively calculating correlation degrees; when the association degrees are lower than a preset threshold value, intercepting the customer service voice; recognizing the voice emotion of the customer service voice, comparing the voice emotion with a pre-configured screening emotion set, and comparing the customer service text with a pre-configured screening lexicon; when the voice emotion does not belong to the screening emotion set and the customer service text does not comprise words in a screening word library, recognizing a topic of the customer service text; and when the association degree between the customer service text and the call topic is higher than a preset threshold value, or the association degree between the topic of the customer service text and the call topic is higher than the preset threshold value, releasing the customer service voice.

Description

Technical Field

[0001] Multiple embodiments of this specification relate to the field of information technology, and specifically to a customer service call quality control method and system based on speech recognition. Background Art

[0002] Customer service call services cover a wide range of industries. Almost all industries that have direct interactions with customers will involve customer service through calls. This is especially true in areas that rely heavily on customer service to maintain customer satisfaction and loyalty, such as the telecommunications industry, e-commerce industry, and financial services industry. On the one hand, companies can enhance their market competitiveness by providing professional and efficient customer service support, and on the other hand, they also need to track and supervise the service quality during the call service process. High-quality call services can not only establish a good image and promote employee growth, but also reduce operating costs and avoid legal risks. Since call services are instant and difficult to verify quickly, quality management is difficult. At present, users often rate service calls afterwards, but this method is lagging. Therefore, it is necessary to study the control technology of customer service call quality. Summary of the invention

[0003] Multiple embodiments of this specification describe a customer service call quality control method and system based on speech recognition.

[0004] In a first aspect, the embodiments of this specification provide a method for customer service call quality control based on speech recognition, comprising the steps of: Read the voice of the customer service call in real time, and identify the voice as the customer's voice and the customer service voice according to the timbre of the voice; Recognize the texts of the customer voice and the customer service voice, record them as customer text and customer service text respectively, and identify the conversation topic according to the customer text and customer service text; According to the customer text and a pre-configured answer library, a number of optional answers are obtained, and the correlation between the customer service text and the several optional answers is calculated respectively; When the correlation between the customer service text and several optional replies is lower than a preset threshold, intercepting the customer service voice; Identify the voice emotion of the customer service voice, compare the voice emotion with a pre-configured screening emotion set, and compare the customer service text with a pre-configured screening vocabulary; When the voice emotion does not belong to the screening emotion set and the customer service text does not include words in the screening vocabulary, identifying the topic of the customer service text; When the relevance between the customer service text and the conversation topic is higher than a preset threshold, or when the relevance between the topic of the customer service text and the conversation topic is higher than a preset threshold, the customer service voice is released.

[0005] In a second aspect, the embodiment of this specification provides a customer service call quality control system based on speech recognition, including: A reading module reads the voice of the customer service call in real time, and identifies the voice as the customer's voice and the customer service voice according to the timbre of the voice; A recognition module, which recognizes the text of the customer voice and the customer service voice, records them as customer text and customer service text respectively, and recognizes the conversation topic according to the customer text and the customer service text; A reply module, which obtains a plurality of optional replies according to the customer text and a pre-configured reply library, and respectively calculates the relevance between the customer service text and the plurality of optional replies; An interception module, when the correlation between the customer service text and a plurality of the optional replies is lower than a preset threshold, intercepts the customer service voice; A comparison module, which identifies the voice emotion of the customer service voice, compares the voice emotion with a pre-configured screening emotion set, and compares the customer service text with a pre-configured screening vocabulary; A topic module, when the voice emotion does not belong to the screening emotion set and the customer service text does not include words in the screening vocabulary, identifies the topic of the customer service text; The management and control module releases the customer service voice when the relevance between the customer service text and the call topic is higher than a preset threshold, or the relevance between the topic of the customer service text and the call topic is higher than a preset threshold.

[0006] In a third aspect, an embodiment of this specification provides an electronic device, including a processor and a memory; The processor is connected to the memory; The memory is used to store executable program code; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the method described in any one of the above aspects.

[0007] In a fourth aspect, an embodiment of the present specification provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method described in any of the above aspects.

[0008] In a fifth aspect, an embodiment of this specification provides a computer program product, including a computer program, which implements the method described in any of the above aspects when executed by a processor.

[0009] The beneficial effects brought by the technical solutions provided by some embodiments of this specification include at least: In multiple embodiments of this specification, the customer service call quality control method provided is to identify the voice text, distinguish between the customer text and the customer service text, and conduct a preliminary check on the customer service voice in combination with the reply library. When the customer service voice exceeds the reply library, the corresponding steps are initiated to check the call voice and intercept the customer service voice that does not meet the corresponding conditions, which helps to improve the quality of customer service calls, reduce or avoid irregular customer service call content, and ultimately improve service quality. By identifying the similarity and correlation between keywords in customer texts and customer service texts and topics in the topic library, the call topic is more in line with the context, which helps to more reasonably control the call quality.

[0010] Other features and advantages of the various embodiments of the present specification will be further disclosed in the following detailed description and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of this specification, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0012] Figure 1 A schematic diagram of an application scenario of the customer service call quality control method provided in an embodiment of this specification.

[0013] Figure 2 A schematic diagram of the customer service call quality control method architecture provided in an embodiment of this specification.

[0014] Figure 3 This is a schematic diagram of the operation interface provided in the embodiments of this specification.

[0015] Figure 4 A schematic diagram of a customer service call quality control method provided in an embodiment of this specification.

[0016] Figure 5 A schematic diagram of a method for obtaining a comparison correlation degree provided in an embodiment of this specification.

[0017] Figure 6 A schematic diagram of a method for obtaining a reply library provided in an embodiment of this specification.

[0018] Figure 7 Schematic diagram of the customer service call quality control system provided in the embodiments of this specification.

[0019] Figure 8 A schematic diagram of an electronic device provided in an embodiment of this specification. DETAILED DESCRIPTION

[0020] The following is an explanation and description of the technical solutions of the embodiments of this specification in conjunction with the drawings of the embodiments of this specification, but the following embodiments are only preferred embodiments of this specification, not all. Based on the embodiments in the implementation mode, other embodiments obtained by those skilled in the art without creative work are all within the scope of protection of this specification.

[0021] The terms "first", "second", "third", etc. in the description and claims of this specification and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0022] In the following description, terms such as "inside", "outside", "up", "down", "left", "right", etc. that indicate directions or positional relationships are only used to facilitate the description of the embodiments and simplify the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as a limitation of this specification.

[0023] The data involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection of relevant data complies with the relevant laws, regulations and standards of relevant countries and regions.

[0024] Before describing the technical solution in this specification, an introduction is given to the application scenarios and related technologies of the technical solution.

[0025] Call service refers to providing real-time voice communication and support for enterprises and customers 10 through telephone or other voice communication tools. This service is usually provided by a dedicated customer service 33 team, aiming to answer customer questions, handle complaints, provide technical support or guidance, etc. Call service is the most direct and immediate form of customer service, which can quickly respond to customer needs and solve practical problems. The process of call service includes answering the phone, greeting and verification, demand analysis, problem solving, ending the call, recording and feedback. It can directly handle and answer questions, provide operation guidance, handle complaints, record problems and transfer them to relevant departments for processing. It has the function of soothing customers 10 emotions and solving urgent or complex problems. Therefore, enterprises in many industries have opened call services of varying sizes. For example, it can answer customers 10 questions about product functions, usage methods, prices, etc. Provide technical support such as equipment installation, software use, and troubleshooting. Handle order inquiries, modifications, cancellations, etc. Resolve customer 10 complaints and provide reasonable compensation or solutions. Help customers 10 perform account registration, login, password reset, etc. And provide immediate help and support when customers encounter urgent problems.

[0026] Providing services through calls is characterized by immediacy, personalization, strong interactivity, emotional connection, and a wide range of applications. Specifically, call services can immediately respond to customer 10's needs and provide real-time support and assistance, which is faster than other methods such as email and online chat. Through direct conversations, customer service 33 can better understand customer needs and provide personalized services based on customer 10's tone, emotions and other non-verbal information. Voice communication allows both parties to communicate in a two-way manner, which can more effectively clarify problems, confirm solutions, and improve the efficiency of problem solving. During the call, customer service 33 can convey care and warmth through tone, intonation, etc., enhance customer 10's emotional connection, and improve customer satisfaction. Call services are applicable to problems of various complexities. Whether it is a simple consultation or complex troubleshooting, it can be effectively solved over the phone.

[0027] As a direct and efficient way of customer service, call service plays an important role in many industries. Through professional customer service33 and standardized service processes, enterprises can better meet customer needs, improve customer satisfaction and enhance brand loyalty. Therefore, the quality control of call service is of great significance. At present, feedback on call service quality is often obtained through post-rating by customers10, which not only has the disadvantage of lag, but also cannot accurately know which call content causes customer10 dissatisfaction. To this end, this manual provides a customer service call quality control method based on voice recognition, please refer to the attached Figure 1The customer service call quality control method provided in this specification runs on a server 31 and is connected to a customer service call system and a terminal device 34 for background management.

[0028] The customer service 33 is connected to the call service software 32 on the computer through the headset, and the call service software 32 establishes data exchange with the communication device 20, which is usually provided by the telecommunications service provider. The client 10 connects to the communication device 20 through the phone to realize a voice call with the customer service 33. The call service software 32 running on the computer is also connected to the server 31 set up, exchanges data with the server 31, and submits relevant voice, data and status to the server 31. The server 31 has the function of feedback data and sending control instructions to the call service software 32. The manager 35 connects to the server 31 through the terminal device 34 to view relevant logs. Furthermore, a report system can be run on the server 31, so that the manager 35 can directly view the report and understand the call quality of the customer service 33.

[0029] The method provided in this application is applied to Figure 2 The system architecture shown, Figure 2 is a schematic diagram of the system architecture in the embodiment of the present application, such as Figure 2 As shown, the system architecture includes a server 31 and a terminal device 34, and the client (i.e., an interactive interface for viewing relevant quality control data) is deployed on the terminal device 34, wherein the client can be run on the terminal device 34 in the form of a browser, or in the form of an independent application (APP), etc. The specific presentation form of the client is not limited here. Please refer to the attached Figure 3 The client or operation interface can be run on the terminal device 34 in the form of a browser, or in the form of an independent application (APP), etc. The specific presentation form of the client is not limited here. The relevant reports can be viewed on the operation interface. The preparation of the report can be realized by the public technology in the field.

[0030] The manager 35 can use a terminal device 34 such as a PC, a smart phone, or a tablet computer to connect to the server 31. And obtain a log or report on the quality of the customer service call from the server 31. A client can be run on the terminal device 34, or at least an interactive interface can be run. The server 31 involved in this application can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content distribution networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms. The terminal device 34 can be a smart phone, a tablet computer, a laptop, a PDA, a personal computer, a smart speaker, a smart TV, a smart watch, a car device, a wearable device, etc., but is not limited to this. The terminal device 34 and the server 31 can be directly or indirectly connected by wired or wireless communication, and this application is not limited here. The number of servers 31 and terminal devices 34 is also not limited. The solution provided in this application can be completed independently by the terminal device 34, can also be completed independently by the server 31, or can be completed by the terminal device 34 and the server 31 in cooperation. This application does not make any specific limitations on this.

[0031] Since this application involves some professional terms, these professional terms will be introduced below.

[0032] timbre Timbre refers to the characteristics or qualities of sound, including pitch, sound quality, volume and changes in timbre. Timbre is determined by factors such as vocal cord vibration, oral and nasal cavity resonance, etc. People have different timbres, and the timbre can be used to quickly identify the person speaking. Tone recognition extracts the frequency domain features of the voice and distinguishes different timbres by the frequency domain features. In addition, the volume of the voice can be used to assist in distinguishing timbre. Since only the timbre of the customer and the customer service needs to be distinguished, the error tolerance for timbre recognition is high.

[0033] Call topic The topic of a call refers to the specific content or subject discussed during a call. A topic of a call can be a question, request, or complaint raised by a customer, or information, suggestions, or solutions provided by customer service 33. The topic can be submitted to the large language model by establishing a natural language task based on the customer text and customer service text, and the response of the large language model is obtained. It is also possible to preset several call topics and provide them to the large language model at the same time, requiring the large language model to make the most matching selection from the provided call topics.

[0034] Voice emotion Voice emotion refers to the emotional state conveyed by voice, including happiness, anger, sadness, anxiety, calmness, etc. Voice emotion can be expressed through changes in pitch, speech speed, volume, etc. The recognition of voice emotion can be carried out using the publicly available technology in the art. Exemplarily, it is implemented using a trained machine learning model, specifically including extracting characteristic parameters of voice, such as pitch, sound intensity, speech speed, pauses, manually annotating voice emotions to form sample data, and training the machine learning model with the sample data to obtain a machine learning model that can recognize voice emotions.

[0035] Large Language Model Large language models are deep learning models with large parameter counts that are designed to understand and generate natural language. These models learn patterns and structures in language by training on large amounts of text data, enabling them to perform a variety of natural language tasks such as machine translation, text summarization, sentiment analysis, question-answering systems, etc. Large language models have millions or even billions of parameters, which enables them to capture very complex patterns in language.

[0036] Natural language tasks Natural language tasks refer to natural language processing. Natural language processing (NLP) is an interdisciplinary subject in the fields of computer science, artificial intelligence, and linguistics, which aims to enable computers to understand, interpret, and generate human language. Natural language tasks (NLP tasks) refer to a series of language-related tasks completed through algorithms and technologies.

[0037] This manual first provides a customer service call quality control method based on voice recognition. Figure 4 , including the steps of: Step S102) Read the voice of the customer service call in real time, and identify the voice as the customer voice and the customer service voice according to the timbre of the voice. Identify the voices of different people by timbre, and then distinguish the customer service voice and the customer voice, and further divide the voice into the customer voice and the customer service voice. Since it is only necessary to distinguish the customer voice and the customer service voice, the sound size can be used to assist in the distinction of timbre, specifically using the sound size to preliminarily judge the distinction between the customer voice and the customer service voice, and then use the timbre comparison. Compared with identifying the timbre, it can improve the execution efficiency. Since on a specific call service software 32, the customer service is fixed or the frequency of change is low, it is easier to distinguish the customer service voice. The essence of timbre is the difference in the resonant frequency composition in the frequency domain. Therefore, after extracting the frequency domain features of the voice, according to the comparison of the frequency domain composition, it is possible to quickly complete the judgment of whether it is the timbre of the identified customer service. And it is possible to complete the judgment of whether the timbre of two voices is the same.

[0038] Step S104) Identify the text of the customer voice and customer service voice, record them as customer text and customer service text respectively, and identify the conversation topic based on the customer text and customer service text. After identifying the customer voice and customer service voice, further identify the text based on the voice. Audio recognition of text can be performed using the technology already disclosed in the art. The conversation topic is identified based on the customer text and customer service text with the help of a large language model.

[0039] Exemplary, the customer text and customer service text are as follows: Customer: Hello, I would like to know more about your air fryer, specifically model A123.

[0040] Customer Service: Hello, thank you for your call. Model A123 air fryer is one of our best-selling products.

[0041] Customer: What is the capacity of this air fryer? How many people in the family can it be used for? Customer Service: The capacity of this air fryer is 5 liters, suitable for a family of 2-4 people.

[0042] Customer: How fast does it heat up? Is there a preheating function? Customer Service: It heats up very quickly. Generally, the preheating time only takes 3-5 minutes. It also has a preheating function, and the preheating time can be set in advance.

[0043] Customer: I also want to know whether it is easy to clean and maintain, and is there anything I need to pay attention to? Customer Service: In terms of cleaning and maintenance, the inner pot of this air fryer is non-stick coated, which is very easy to clean. It is recommended to wipe it with a damp cloth after each use and clean the filter regularly.

[0044] Customer: How much does this air fryer cost? Are there any promotions? Customer Service: Regarding the price, the model A123 air fryer is priced at 999 yuan. We are currently running a promotion where you can get a premium baking tray and a cookbook for free with your purchase.

[0045] The conversation topics of customer texts obtained through large language model recognition are "consulting about model A123 air fryer", "consulting about specifications", "consulting about technical indicators", "consulting about usage suggestions", and "consulting about price promotions". The conversation topics of customer service texts obtained through large language model recognition are "introducing products", "introducing specifications", "introducing technical indicators", "introducing usage and maintenance", and "introducing prices and promotions".

[0046] Step S106) According to the customer text and the pre-configured answer library, a number of optional answers are obtained, and the correlation between the customer service text and the several optional answers is calculated respectively.

[0047] For example, for the customer text "How fast is it heating? Is there a preheating function?", the reply library contains optional reply one "The heating speed is XXX, and the preheating function is XXX", optional reply two "The product introduction page has a detailed introduction, and the content of the product introduction page is a true introduction, you can use it as a reference", and optional reply three "Please wait, I will look up the relevant information and get back to you".

[0048] Step S108) When the correlation between the customer service text and several optional replies is lower than a preset threshold, intercept the subsequent customer service voice.

[0049] When the customer service's reply exceeds the aforementioned three optional replies, that is, the correlation with the three optional replies is lower than the preset threshold, it will be determined that the customer service's response has the risk of irrelevant answers, and the subsequent customer service voice is intercepted. Because the analyzed customer service voice has actually been transmitted to the customer by the communication device 20, it is too late to intercept it, so the subsequent customer service voice is intercepted to prevent the possible problematic replies from continuing.

[0050] Step S110) identifying the voice emotion of the subsequent customer service speech, comparing the voice emotion with a pre-configured screening emotion set, identifying the text of the subsequent customer service speech as a subsequent customer service text, and comparing the subsequent customer service text with a pre-configured screening vocabulary.

[0051] Exemplarily, for the customer text "How fast does it heat up? Is there a preheating function?", the customer service text is "Such a simple question, you don't need my answer". After identification, the voice emotion is obtained as "impatience". Exemplarily, the screening emotion set is {"anger", "contempt", "dissatisfaction", "impatience", "disappointment", "confusion", "indifference", "anxiety"}. Therefore, the customer service's reply is "Such a simple question, you don't need my answer". Because the identified voice emotion is "impatience", the customer service voice will remain intercepted. At this time, it can be processed by the publicly available technology in the field or by means of non-technical fields. Exemplarily, technical means are used to remind the customer service to pay attention to standardized replies, or management means are used to switch the customer to other customer service for processing after being prompted.

[0052] As another example, for the customer text "How is its heating speed? Is there a preheating function?", the customer service text is "Heating speed is not the most important technical indicator of an air fryer. Temperature control stability and uniformity are relatively more important. This model of air fryer uses the latest algorithm and optimized air duct, which has more accurate temperature control and more uniform temperature distribution." After emotion recognition, the voice emotion is obtained as "stable", and there are no words in the screening vocabulary, so the customer service voice is released and transmitted to the customer through the communication device 20. And the customer service voice will not be intercepted until the next time the correlation between the customer service and the optional replies is lower than the preset threshold.

[0053] Step S112) When the voice emotion does not belong to the screening emotion set and the subsequent customer service text does not include words in the screening vocabulary, identify the topic of the subsequent customer service text. When the customer service text includes words in the screening vocabulary, the corresponding customer service voice will be intercepted.

[0054] Step S114) When the correlation between the subsequent customer service text and the conversation topic is higher than the preset threshold, or the correlation between the topic of the subsequent customer service text and the conversation topic is higher than the preset threshold, the subsequent customer service voice is released. When the customer service voice does not contain the emotions in the screening emotion set, nor does it include the words in the screening vocabulary, it is also necessary to satisfy that the correlation between the customer service text and the conversation topic is higher than the preset threshold. Exemplarily, the conversation topic of the customer text "How is its heating speed? Is there a preheating function?" is "Introducing technical indicators". The customer service text "Heating speed is not the most important technical indicator of the air fryer, and temperature control stability and uniformity are relatively more important. This model of air fryer uses the latest algorithm and optimized air duct, with more accurate temperature control and more uniform temperature distribution", is associated with "Introducing technical indicators", that is, the correlation is higher than the preset threshold (although it is not a direct answer to the heating speed, it is an answer with correlation), so the subsequent customer service voice processing is released. All thresholds in this embodiment can be set by the technology disclosed in the art, or manually set.

[0055] On the other hand, in another embodiment, the method for identifying the call topic according to the customer text and the customer service text includes: Try to group the plurality of customer texts and customer service texts into one group; Identify several keywords of the customer text and customer service text in the group; Compare the keywords with topics in a preset topic library to obtain topic relevance and the maximum relevance value among the topic relevances; Comparing the topic corresponding to the maximum correlation value with the previously identified conversation topic to obtain a comparison correlation degree; When the maximum correlation value is greater than a preset threshold or the comparison correlation degree is greater than a preset threshold, the topic corresponding to the maximum correlation value is used as the conversation topic; When the maximum correlation value is not greater than a preset threshold and the comparison correlation degree is not greater than a preset threshold, an attempt is made to group the plurality of customer texts and customer service texts again.

[0056] It is a special grouping method to group each customer voice and customer service voice separately for call topic recognition, that is, each group has only one customer voice or customer service voice. As an improved method, try to group two or more customer voices and customer service voices into one group and recognize the call topic together. It can improve the accuracy of call topic recognition. For example, "Customer: Hello, I want to know about your air fryer, especially model A123" and "Customer Service: Hello, thank you for your call. Model A123 air fryer is one of our hot-selling products" are grouped together. For example, the topic library is {"Introduce the product", "Price discount", "After-sales", "Instructions for use and maintenance", "Recipe introduction", "Logistics consultation"}.

[0057] The keywords obtained are "air fryer", "model A123", and "hot-selling products". First, the large language model is used to obtain the association value between "air fryer" and each topic in the topic library {"introducing products", "price discounts", "after-sales service", "instructions for use and maintenance", "recipe introduction", and "logistics consultation"}. The association value between "air fryer" and "introducing products" is 0.5, the association value between "air fryer" and "price discounts" is 0.4, the association value between "air fryer" and "after-sales service" is 0.3, the association value between "air fryer" and "instructions for use and maintenance" is 0.4, the association value between "air fryer" and "recipe introduction" is 0.3, and the association value between "air fryer" and "logistics consultation" is 0.2. That is, the maximum association value in the topic association is 0.5, and the corresponding topic is "introducing products". Similarly, the topic corresponding to the maximum association value between "model A123" and "hot-selling products" and the topic library is also "introducing products". Finally, the conversation topic of this group is obtained as "introducing products".

[0058] As another example, "Customer: What is the capacity of this air fryer? How many people in the family is it suitable for?", "Customer Service: The capacity of this air fryer is 5 liters, suitable for a family of 2-4 people", "Customer: How is its heating speed? Is there a preheating function?", "Customer Service: It heats up very quickly, and the general preheating time only takes 3-5 minutes. It also has a preheating function, and the preheating time can be set in advance" are grouped together. The keywords that can be obtained are "capacity", "family use", "heating speed", "preheating", and "function". The correlation values ​​of these keywords with the topics in the topic library are all lower than the preset threshold. For example, the threshold is 0.5. When the correlation values ​​are all lower than 0.5, the topic corresponding to the maximum correlation value is "Instructions for use and maintenance". The comparison correlation degree between the "Instructions for use and maintenance" and the previously identified call topic "Introduction to the product" is 0.8.

[0059] It can be seen that although the maximum correlation value between the topic "Instructions for use and maintenance" and the keywords in the group is not higher than 0.5, the comparison correlation between the topic "Instructions for use and maintenance" and the previous conversation topic "Introducing the product" is 0.8, which is sufficient to show that the topic "Instructions for use and maintenance" is suitable as the conversation topic of this group.

[0060] On the other hand, this specification provides a specific method for comparing the topic corresponding to the maximum correlation value with the previously identified call topic to obtain the comparison correlation degree, please refer to the attached Figure 5 ,include: Step S202) reading the voice of the historical customer service call, recognizing the text of the voice, and recording it as the historical text; Step S204) identifying a number of keywords of the historical text, and grouping the historical text according to the keywords; Step S206) obtaining the topic of the grouped historical text based on the comparison of the grouped keywords with the preset topic library, and recording it as the grouped topic; Step S208) Arrange all grouped topics in chronological order to obtain a historical topic sequence; Step S210) Repeat the above steps to obtain multiple historical topic sequences; Step S212) Pairing two adjacent historical topics in the historical topic sequence as a topic pair; Step S214) Obtaining the relevance of historical topics according to the frequency of occurrence of the topic pair in the entire historical topic sequence; Step S216) respectively obtain the topic corresponding to the maximum correlation value and two historical topics closest to the previously identified call topic, and obtain a comparison correlation degree according to the correlation degree of the two closest historical topics.

[0061] The comparison correlation can be directly obtained through a large language model. However, the large language model lacks field specificity. Through the method provided in this embodiment, a comparison correlation that is more suitable for the industry field can be obtained based on the historical texts in the industry field. Among them, the method for obtaining the correlation between the topic of the customer service text and the topic of the call includes: respectively obtaining the two historical topics that are closest to the topic of the customer service text and the topic of the call, and obtaining the correlation between the topic of the customer service text and the topic of the call based on the correlation between the two closest historical topics.

[0062] On the other hand, this manual provides a method to configure the reply library, see the attached Figure 6 ,include: Step S302) reading the voice of the historical customer service call, recognizing the text of the voice, and recording it as the historical text; Step S304) extracting the customer text and customer service text from the historical text; Step S306) picking up keywords of the customer text and recording them as customer keywords, and classifying the customer text according to the customer keywords; Step S308) Obtaining customer questions based on the customer texts of the same category; Step S310) obtaining a plurality of customer service texts that are adjacent to the customer text of the same category, and obtaining a plurality of answers to the customer question based on the plurality of customer service texts; Step S312) Obtain a response library based on the customer question and the corresponding plurality of responses.

[0063] The reply library configured using a large language model lacks pertinence, and manually setting up a reply library is inefficient. By classifying customer texts based on keywords, similar questions or similar topics asked by customers can be classified into one category. The reply library is obtained based on all historical customer service texts under this category, which can be more in line with the situation in this industry field, improve the relevance of the replies in the reply library to the customer texts, and help improve the quality of replies. Among them, the method of obtaining customer questions based on the customer texts of the same category includes: generating customer questions based on the keywords of the customer texts of the same category and the pre-connected large language model.

[0064] On the other hand, please refer to the attached Figure 7 ,include: The reading module 100 reads the voice of the customer service call in real time, and identifies the voice as the customer voice and the customer service voice according to the timbre of the voice; The recognition module 200 recognizes the text of the customer voice and the customer service voice, which are recorded as customer text and customer service text respectively, and recognizes the conversation topic according to the customer text and the customer service text; The reply module 300 obtains a plurality of optional replies according to the customer text and a pre-configured reply library, and respectively calculates the relevance between the customer service text and the plurality of optional replies; The interception module 400 intercepts the customer service voice when the correlation between the customer service text and the plurality of optional replies is lower than a preset threshold; A comparison module 500 is used to identify the voice emotion of the customer service voice, compare the voice emotion with a pre-configured screening emotion set, and compare the customer service text with a pre-configured screening vocabulary; Topic module 600, when the voice emotion does not belong to the screening emotion set and the customer service text does not include words in the screening vocabulary, identifying the topic of the customer service text; The management and control module 700 releases the customer service voice when the relevance between the customer service text and the call topic is higher than a preset threshold, or the relevance between the topic of the customer service text and the call topic is higher than a preset threshold.

[0065] See also Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of this specification is shown.

[0066] like Figure 8 As shown, the electronic device 1100 may include: at least one processor 1101, at least one network interface 1104, a user interface 1103, a memory 1105 and at least one communication bus 1102. Among them, the communication bus 1102 can be used to realize the connection and communication of the above-mentioned components. Among them, the user interface 1103 may include a button, and the optional user interface may also include a standard wired interface and a wireless interface. Among them, the network interface 1104 may include but is not limited to a Bluetooth module, an NFC module, a Wi-Fi module, etc. Among them, the processor 1101 may include one or more processing cores. The processor 1101 uses various interfaces and lines to connect various parts in the entire electronic device 1100, and executes various functions and processes data of the routing device 1100 by running or executing instructions, programs, code sets or instruction sets stored in the memory 1105, and calling data stored in the memory 1105. Optionally, the processor 1101 can be implemented in at least one hardware form of DSP, FPGA, and PLA. The processor 1101 may integrate one or a combination of CPU, GPU and modem, etc. Among them, the CPU mainly processes the operating system, user interface and application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communication.

[0067] It is understandable that the above-mentioned modem may not be integrated into the processor 1101, but may be implemented by a separate chip.

[0068] Among them, the memory 1105 may include RAM or ROM. Optionally, the memory 1105 includes a non-transitory computer-readable medium. The memory 1105 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 1105 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 1105 may also be at least one storage device located away from the aforementioned processor 1101. The memory 1105 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application. The processor 1101 may be used to call the application stored in the memory 1105 and execute the methods in the above-mentioned multiple embodiments.

[0069] The embodiments of this specification also provide a computer-readable storage medium, which stores instructions, and when the instructions are executed on a computer or a processor, the computer or the processor executes the multiple steps in the above embodiments. If the components of the above electronic device are implemented in the form of software functional units and sold or used as independent products, they can be stored in the computer-readable storage medium.

[0070] The embodiments of this specification also provide a computer program product, including a computer program, which implements multiple steps in the above embodiments when executed by a processor.

[0071] In the absence of conflict, the technical features in this embodiment and implementation scheme can be combined arbitrarily.

[0072] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes a plurality of computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of this specification is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions may be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes multiple available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital versatile disc (DVD)), or a semiconductor medium (eg, a solid state drive (SSD)).

[0073] When implemented by hardware or firmware, the aforementioned method flow is programmed into the hardware circuit to obtain the corresponding hardware circuit structure and realize the corresponding function. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit, and its logic function is determined by the user programming the device. The designer programs by himself to "integrate" a digital system on a PLD, without asking a chip manufacturer to design and make a dedicated integrated circuit chip. Moreover, nowadays, instead of manually making integrated circuit chips, this programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when writing program development, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL), and HDL is not just one, but many. Those skilled in the art should also be aware that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit to easily obtain the hardware circuit that implements the logic method flow.

[0074] The embodiments described above are merely preferred embodiments of this specification and are not intended to limit the scope of this specification. Without departing from the design spirit of this specification, various modifications and improvements made to the technical solutions of this specification by ordinary technicians in this field should fall within the scope of protection determined by the claims of this specification.

Claims

1. A customer service call quality control method based on speech recognition, characterized in that: Includes steps: Read the voice of the customer service call in real time, and identify the voice as the customer's voice and the customer service voice according to the timbre of the voice; Recognize the texts of the customer voice and the customer service voice, record them as customer text and customer service text respectively, and identify the conversation topic according to the customer text and customer service text; According to the customer text and a pre-configured answer library, a number of optional answers are obtained, and the correlation between the customer service text and the several optional answers is calculated respectively; When the correlation between the customer service text and several optional replies is lower than a preset threshold, intercepting subsequent customer service voice; Identify the speech emotion of the subsequent customer service speech, compare the speech emotion with a pre-configured screening emotion set, identify the text of the subsequent customer service speech as a subsequent customer service text, and compare the subsequent customer service text with a pre-configured screening vocabulary; When the voice emotion does not belong to the screening emotion set and the subsequent customer service text does not include words in the screening vocabulary, identifying the topic of the subsequent customer service text; When the relevance between the subsequent customer service text and the conversation topic is higher than a preset threshold, or when the relevance between the topic of the subsequent customer service text and the conversation topic is higher than a preset threshold, the subsequent customer service voice is released.

2. The customer service call quality control method based on speech recognition according to claim 1 is characterized in that: The method for identifying the conversation topic according to the customer text and the customer service text includes: Try to group the plurality of customer texts and customer service texts into one group; Identify several keywords of the customer text and customer service text in the group; Compare the keywords with topics in a preset topic library to obtain topic relevance and the maximum relevance value among the topic relevances; Comparing the topic corresponding to the maximum correlation value with the previously identified conversation topic to obtain a comparison correlation degree; When the maximum correlation value is greater than a preset threshold or the comparison correlation degree is greater than a preset threshold, the topic corresponding to the maximum correlation value is used as the conversation topic; When the maximum correlation value is not greater than a preset threshold and the comparison correlation degree is not greater than a preset threshold, an attempt is made to group the plurality of customer texts and customer service texts again.

3. The customer service call quality control method based on speech recognition according to claim 2 is characterized in that: The method of comparing the topic corresponding to the maximum correlation value with the previously identified conversation topic to obtain the comparison correlation includes: Read the voice of historical customer service calls, recognize the text of the voice, and record it as historical text; Identifying a number of keywords of the historical text, and grouping the historical text according to the keywords; According to the comparison between the grouped keywords and a preset topic library, the topic of the grouped historical text is obtained and recorded as the grouped topic; All grouped topics are arranged in chronological order to obtain a historical topic sequence; Repeat the above steps to obtain multiple historical topic sequences; Pairing two adjacent historical topics in the historical topic sequence as a topic pair; Obtain the relevance of historical topics according to the frequency of topic pairs appearing in the entire historical topic sequence; The topic corresponding to the maximum correlation value and two historical topics closest to the previously identified conversation topic are respectively obtained, and a comparison correlation is obtained according to the correlation between the two closest historical topics.

4. The customer service call quality control method based on speech recognition according to claim 3 is characterized in that: The method for obtaining the relevance between the topic of the customer service text and the call topic includes: Two historical topics closest to the topic of the customer service text and the topic of the conversation are respectively obtained, and the degree of relevance between the topic of the customer service text and the topic of the conversation is obtained according to the degree of relevance between the two closest historical topics.

5. The customer service call quality control method based on speech recognition according to any one of claims 1 to 4, characterized in that: Methods for configuring the reply library include: Read the voice of historical customer service calls, recognize the text of the voice, and record it as historical text; Extracting customer text and customer service text from the historical text; The keywords of the customer text are mentioned as customer keywords, and the customer text is classified according to the customer keywords; Obtaining customer questions based on the customer texts of the same category; Obtaining a plurality of customer service texts that are adjacent to the customer text of the same category, and obtaining a plurality of answers to the customer question based on the plurality of customer service texts; According to the customer questions and the corresponding plurality of replies, a reply library is obtained.

6. The customer service call quality control method based on speech recognition according to claim 5 is characterized in that: According to the customer text of the same category, the method of obtaining the customer's question includes: The customer questions are generated based on the keywords of the customer text of the same category and the pre-connected large language model.

7. The customer service call quality control system based on speech recognition is characterized by: include: A reading module reads the voice of the customer service call in real time, and identifies the voice as the customer's voice and the customer service voice according to the timbre of the voice; A recognition module, which recognizes the text of the customer voice and the customer service voice, records them as customer text and customer service text respectively, and recognizes the conversation topic according to the customer text and the customer service text; A reply module, which obtains a plurality of optional replies according to the customer text and a pre-configured reply library, and respectively calculates the relevance between the customer service text and the plurality of optional replies; An interception module, when the correlation between the customer service text and a plurality of the optional replies is lower than a preset threshold, intercepts the customer service voice; A comparison module, which identifies the voice emotion of the customer service voice, compares the voice emotion with a pre-configured screening emotion set, and compares the customer service text with a pre-configured screening vocabulary; A topic module, when the voice emotion does not belong to the screening emotion set and the customer service text does not include words in the screening vocabulary, identifies the topic of the customer service text; The management and control module releases the customer service voice when the relevance between the customer service text and the call topic is higher than a preset threshold, or the relevance between the topic of the customer service text and the call topic is higher than a preset threshold.

8. An electronic device, characterized in that including a processor and a memory; The processor is connected to the memory; The memory is used to store executable program code; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. Computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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