WO2020253115A1 - Procédé, appareil et dispositif de recommandation de produit basés sur une reconnaissance vocale et support de stockage - Google Patents

Procédé, appareil et dispositif de recommandation de produit basés sur une reconnaissance vocale et support de stockage Download PDF

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Publication number
WO2020253115A1
WO2020253115A1 PCT/CN2019/121198 CN2019121198W WO2020253115A1 WO 2020253115 A1 WO2020253115 A1 WO 2020253115A1 CN 2019121198 W CN2019121198 W CN 2019121198W WO 2020253115 A1 WO2020253115 A1 WO 2020253115A1
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WIPO (PCT)
Prior art keywords
customer
data stream
sales
voice
text
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PCT/CN2019/121198
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English (en)
Chinese (zh)
Inventor
刘金满
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深圳壹账通智能科技有限公司
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Publication of WO2020253115A1 publication Critical patent/WO2020253115A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0631—Item recommendations
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/26—Speech to text systems
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L17/00—Speaker identification or verification
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
    • G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use
    • G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination
    • G10L25/63—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination for estimating an emotional state

Definitions

  • This application relates to the field of e-commerce, and in particular to a method, device, equipment and storage medium for product recommendation based on voice recognition.
  • the main purpose of this application is to provide a product recommendation method, device, equipment, and storage medium based on voice recognition, which aims to solve the technical problem of inaccurate customer demand analysis during current telemarketing.
  • the present application provides a method for product recommendation based on voice recognition.
  • the method for product recommendation based on voice recognition includes the following steps:
  • the step of processing the voice information to generate a customer data stream and a sales data stream includes:
  • the sales voice information is recognized through the preset voice processing model to obtain corresponding sales text data and sales voice feature data, and the sales text data and the sales voice feature data are sorted in a time sequence to generate a sales data stream.
  • the present application also provides a voice recognition-based product recommendation device, and the voice recognition-based product recommendation device includes:
  • the voice processing module is used to process the voice information to generate a customer data stream and a sales data stream when the voice information sent by the terminal is received;
  • the detection and analysis module is configured to obtain the time node and the customer attention text corresponding to the positive emotional fluctuation when a positive emotional fluctuation is detected in the customer data stream;
  • a retrospective acquisition module configured to trace the sales data stream according to the time node and the customer attention text, and acquire target sales text data in the sales data stream that causes the positive mood fluctuations;
  • the acquiring and sending module is configured to acquire the product information corresponding to the target sales text data in the preset product database, and send the product information to the terminal, so that the sales personnel corresponding to the terminal can introduce the product information according to the product information.
  • this application also provides a product recommendation device based on voice recognition
  • the voice recognition-based product recommendation device includes: a memory, a processor, and computer-readable instructions stored on the memory and running on the processor, wherein:
  • this application also provides a computer storage medium
  • the computer storage medium stores computer readable instructions, and when the computer readable instructions are executed by a processor, the steps of the above-mentioned voice recognition-based product recommendation method are realized.
  • the method, device, device, and storage medium for product recommendation based on voice recognition proposed in the embodiments of this application, when the server receives the voice information sent by the terminal, processes the voice information to generate a customer data stream and a sales data stream.
  • the voice information is divided into customer data streams and sales data streams, and processed separately for customer data streams and sales data streams to achieve detailed analysis.
  • the server when positive emotional fluctuations in the customer data stream are detected, the server obtains The time node corresponding to the positive emotion fluctuation and the customer attention text, and then the server traces the sales data stream according to the time node and the customer attention text to determine the target sales text data that causes the customer’s positive emotion fluctuation, And obtain the product information corresponding to the target sales text data in the preset product database, and send the product information to the terminal, so that the corresponding sales staff of the terminal can introduce the product information according to the product information, which realizes the accurate user Demand analysis, and effective product recommendation.
  • FIG. 1 is a schematic diagram of the device structure of the hardware operating environment involved in the solution of the embodiment of the present application;
  • FIG. 2 is a schematic flowchart of a first embodiment of a product recommendation method based on speech recognition in this application;
  • FIG. 3 is a schematic diagram of functional modules of an embodiment of a product recommendation device based on voice recognition in this application.
  • Figure 1 is the server of the hardware operating environment involved in the solution of the embodiment of the application (also called the product recommendation device based on voice recognition, where the product recommendation device based on voice recognition can be a separate voice recognition-based product recommendation device
  • the product recommendation device is constituted by a combination of other devices and a product recommendation device based on voice recognition).
  • the server in the embodiment of the present application refers to a computer that manages resources and provides services for users, and is generally divided into a file server, a database server, and an application-readable instruction server.
  • the computer or computer system running the above software is also called a server.
  • the server may include: a processor 1001, such as a central processing unit (Central Processing Unit, CPU), network interface 1004, user interface 1003, memory 1005, communication bus 1002, chipset, disk system, network and other hardware.
  • the communication bus 1002 is used to implement connection and communication between these components.
  • the user interface 1003 may include a display screen (Display) and an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface.
  • the network interface 1004 may optionally include a standard wired interface and a wireless interface (such as WIreless-FIdelity, WIFI interface).
  • the memory 1005 may be a high-speed random access memory (random access memory, RAM), or stable memory (non-volatile memory), such as disk storage.
  • the memory 1005 may also be a storage device independent of the foregoing processor 1001.
  • the server may also include a camera, RF (Radio Frequency, radio frequency) circuit, sensor, audio circuit, WiFi module; input unit, display screen, touch screen; network interface can be selected except WiFi, Bluetooth, probe, etc.
  • RF Radio Frequency, radio frequency
  • the server structure shown in FIG. 1 does not constitute a limitation on the server, and may include more or fewer components than shown in the figure, or a combination of certain components, or different component arrangements.
  • the computer software product is stored in a storage medium (storage medium: also called computer storage medium, computer medium, readable medium, readable storage medium, computer readable storage medium, or directly called medium, etc., storage medium
  • storage medium can be a non-volatile readable storage medium, such as RAM, magnetic disk, optical disk, and includes several instructions to make a terminal device (can be a mobile phone, computer, server, air conditioner, or network device, etc.) execute this application
  • the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and computer-readable instructions.
  • the network interface 1004 is mainly used to connect to the back-end database and perform data communication with the back-end database;
  • the user interface 1003 is mainly used to connect to the client (the client, also called the user terminal or the terminal, the embodiment of the application
  • the terminal can be a fixed terminal or a mobile terminal, such as a PC, a smart phone, a tablet computer, an e-book reader, a portable computer, etc.
  • the terminal contains sensors such as light sensors, motion sensors and other sensors, which will not be repeated here), Perform data communication with the client; and the processor 1001 may be used to call computer-readable instructions stored in the memory 1005, and execute the steps in the voice recognition-based product recommendation method provided in the following embodiments of the present application.
  • the first embodiment of the present application provides a product recommendation method based on voice recognition, which is applied to the server shown in FIG. 1.
  • the product recommendation method based on voice recognition includes:
  • Step S10 When the voice information sent by the terminal is received, the voice information is processed to generate a customer data stream and a sales data stream.
  • the sales staff conduct telephone product sales through the terminal.
  • the terminal collects the voice information of the call.
  • the voice information of the call includes: the sales voice information of the salesperson and the customer voice information of the customer.
  • the terminal sends the collected voice information to the server, and the server receives
  • the voice information sent by the terminal and the voice information received by the server processing specifically, include:
  • Step S11 When the voice information sent by the terminal is received, the voice information is recognized through a preset voice processing model to obtain the voiceprint feature corresponding to the voice information, and the voice information is divided into Customer voice messages and sales voice messages.
  • the server receives the voice information sent by the terminal, that is, the voice recognition model is preset in the server, and the preset voice recognition model is the voice recognition algorithm obtained by preset training.
  • the voice recognition algorithm can realize the voice recognition of the voice information.
  • the server uses the preset voice recognition model to extract voice feature data in the voice information, and determines the voice feature of the voice information based on the extracted voice feature data.
  • the server converts the voice information according to the voice feature data. Divided into customer voice information and sales voice information.
  • the server in this embodiment also divides the voice information into sales voice information or customer voice information according to other principles, which will not be repeated in this embodiment.
  • the server divides the voice information according to the voice content of the voice information.
  • the voice content is: I am the xxx server of the xxx company to determine that the voice information is sales voice information.
  • Step S12 Recognize the customer voice information through the preset voice processing model, obtain corresponding customer text data and customer voice feature data, and sort the customer text data and the customer voice feature data in a time sequence to generate customer data flow.
  • the server After the server divides the voice information into customer voice information and sales voice information, the server processes the customer voice information to generate a customer data stream; specifically, the server inputs the customer voice information into the preset voice recognition model, and the preset voice recognition model first The customer voice information is denoised, and then each frame of the customer voice information is recognized as a state, and the states are further combined into phonemes. Finally, each phoneme is combined into words to generate the customer text data corresponding to the customer voice information. The server converts the customer text The data is sorted by time to generate a customer text data stream.
  • the preset voice processing model extracts the customer voice feature data from the customer voice information.
  • the customer voice feature data includes the frequency, pitch, amplitude, etc. of the customer voice.
  • the server sorts the customer voice feature data according to time to obtain the customer voice feature Data stream, the server combines the customer text data stream and the customer voice feature data stream in chronological order to obtain the customer data stream; it can be understood that the customer data stream includes the customer voice feature data stream and the customer text data stream.
  • the customer can be The data stream is analogous to the music score.
  • the customer voice feature data stream in the customer data stream is equivalent to the notes in the music score
  • the customer text data stream in the customer data stream is equivalent to the lyrics in the music score.
  • Step S13 Recognize the sales voice information through the preset voice processing model to obtain corresponding sales text data and sales voice feature data, and sort the sales text data and the sales voice feature data in a time sequence to generate sales data flow.
  • the server processes the sales voice information to generate a sales data stream; specifically, the server inputs the sales voice information into a preset voice recognition model, and the preset voice recognition model first denoises the sales voice information, and then transfers the sales voice Each frame of the information is recognized as a state, and the states are further combined into phonemes, and finally each phoneme is combined into words to generate sales text data corresponding to the sales voice information.
  • the server sorts the sales text data according to time to generate a sales text data stream;
  • the preset voice processing model extracts the sales voice feature data in the sales voice information, where the sales voice feature data is the frequency, pitch, amplitude, etc. of the salesperson’s voice, and the server sorts the sales voice feature data according to time to obtain Sales voice feature data stream; the server combines the sales text data stream and the sales voice feature data stream in chronological order to obtain the sales data stream.
  • voice information is processed to generate customer data streams and sales data streams to facilitate accurate and detailed analysis during subsequent voice analysis.
  • voice information is processed to generate customer data streams and sales data streams to facilitate accurate and detailed analysis during subsequent voice analysis.
  • step S20 when it is detected that a positive emotion fluctuation occurs in the customer data stream, a time node and a customer attention text corresponding to the positive emotion fluctuation are obtained.
  • the server can analyze the customer data stream in different ways:
  • method 1 The server performs analysis based on the client text data stream in the client data stream, specifically, including:
  • Step a Compare the customer text data in the customer data stream with the target words in the preset target vocabulary.
  • Step b When there is target customer text data matching the target word, it is determined that positive mood fluctuations occur in the customer data stream.
  • Step c Use the target customer text data as customer attention text, and obtain the time node corresponding to the customer attention text in the customer data stream.
  • the server obtains the client text data stream in the client data stream, and the server compares the client text data in the client text data stream with the target words in the preset target vocabulary, where the preset target vocabulary refers to the advance
  • the database is set to store the target words.
  • the target words in the preset target word database refer to words related to the product, for example, the target words are: performance, price, etc.
  • the positive emotion fluctuations in this embodiment of the application refer to the customer
  • the server takes the target customer text data corresponding node as the positive emotional fluctuation point
  • the server takes the target customer text data as the customer attention text
  • the server performs a combined analysis based on the customer voice feature data stream and the customer text data stream in the customer data stream, specifically, including:
  • the server obtains each voice feature data in the customer voice feature data stream, and the server sets the voice feature data higher than the preset voice feature data (the preset voice feature data refers to the user's usual voice frequency, pitch, and timbre set in advance)
  • the time point is regarded as the point of positive mood fluctuation.
  • the positive mood fluctuation in the embodiment of this application refers to the change of voice feature data such as question tone appearing in the customer voice information when the customer is interested in the product, and the server obtains the customer voice The time node corresponding to the positive emotion fluctuation in the characteristic data stream, and then the server obtains the customer attention text of the time node in the customer text data stream.
  • Step S30 Trace the sales data stream according to the time node and the customer attention text, and obtain target sales text data in the sales data stream that causes the positive mood swing.
  • Step a Obtain a target sales text data stream for a preset time period before the time node in the sales text data stream, and compare the sales text data in the target sales text data stream with the customer attention text;
  • Step b Obtain target sales text data matching the customer focus text.
  • the server obtains the target sales text data stream for a preset time period before the time node in the sales text data stream, where the preset time period refers to a preset time interval, and the preset time period can be flexibly set according to specific scenarios, for example,
  • the preset time period is set to 1 minute, that is, when the server determines that the time node of the positive mood swing is 15:40:30, the server obtains the target sales data between 15:39:30 and 15:40:30 Stream, the server obtains the target sales text data stream in the target sales data stream, and compares the sales text data in the target sales text data stream with the customer attention text; to determine whether there is a customer attention text match in the target sales text data stream If there is no target sales text data matching the customer focus text in the target sales text data stream, the server sends the customer focus text as prompt information to the terminal so that the sales staff corresponding to the terminal can understand the customer focus text.
  • the server obtains the target sales text data matching the customer's attention text to perform product information query based on the target sales text data, specifically:
  • Step S40 Obtain the product information corresponding to the target sales text data in the preset product database, and send the product information to the terminal, so that a salesperson corresponding to the terminal can introduce the product information according to the product information.
  • the server recommends product information according to the target sales text data.
  • the product database is preset in the server, and the product information is stored in the preset product database.
  • the server queries the preset product database to obtain the target sales text data in the preset product database.
  • the server sends the product information to the terminal for the terminal's corresponding sales staff to introduce the product information.
  • the server converts voice information into sales data streams and customer data streams.
  • the time nodes and customer attention texts corresponding to the positive emotional fluctuations are determined by the server based on the time nodes and The customer pays attention to the text to analyze the sales data flow, and obtains the corresponding product information according to the target sales data, realizes accurate user demand analysis, and effectively introduces products to avoid calls caused by sales staff not understanding user needs or product information The problem of difficult sales.
  • This embodiment is a refinement of step S20 in the first embodiment of the present application.
  • the method in which the server determines the positive mood fluctuations according to the customer voice feature data stream and the customer text data stream in the customer data stream is specifically explained.
  • the product recommendation methods based on speech recognition include:
  • Step S21 Acquire basic feature data in the customer voice feature data stream, and when the customer voice feature data stream is higher than the basic feature data, it is determined that positive mood fluctuations occur in the customer data stream.
  • the server obtains the basic feature data in the customer's voice feature data stream, where the basic feature data is determined by the server according to the voice frequency, amplitude, and pitch in the customer's voice feature data stream. Take a voice feature parameter of frequency as an example for illustration. 10% of the voice feature data stream is less than 30 Hz, 80% of the frequency is 30-50 Hz, and 10% of the frequency is greater than 50 Hz.
  • the server sets the basic feature data to 50 Hz, and the server sets the customer voice feature data stream higher than the basic feature data
  • the target customer’s voice feature data is used as a positive mood swing.
  • Step S22 Obtain the time node corresponding to the positive emotional fluctuation in the customer voice feature data stream, and obtain the customer attention text corresponding to the time node in the customer text data stream.
  • the server obtains the time node corresponding to the positive mood fluctuation in the customer voice characteristic data stream, and the server determines the customer's attention point at this time node, that is, the server obtains the customer attention text corresponding to the time node in the customer text data stream.
  • the server performs accurate customer demand analysis according to the customer data stream and the sales data stream, which improves the accuracy of customer demand analysis.
  • This embodiment is a step after step S40 in the first implementation.
  • the server after the server sends the product information to the terminal, the server detects the customer feedback voice information sent by the terminal, and updates the preset product database according to the customer feedback voice information.
  • the product recommendation method based on voice recognition includes:
  • Step S50 Acquire customer feedback voice information based on the product information, and extract questions of interest from the customer feedback voice information.
  • the salesperson introduces the product information of the terminal.
  • the terminal receives the customer feedback voice information and sends it to the server.
  • the server receives the customer feedback voice information.
  • the customer feedback voice information refers to the customer's announcement based on the salesperson.
  • the server obtains the customer’s feedback voice information and extracts the question of interest, that is, the server obtains the product performance information, product price information or product logistics information in the customer’s feedback voice information as the question of interest.
  • Step S60 Count the number of questions of each interest question, and when the number of questions of interest exceeds a preset threshold, add the interest question to the preset product database.
  • the server counts the number of questions asked for each question of interest, that is, the server records the obtained questions of interest separately, and when there are repetitions, the server accumulates, and the server detects that the number of questions asked for the question of interest exceeds a preset threshold (the preset threshold is preset Set the number of times, the preset threshold can be set according to specific conditions, for example, when the preset threshold is set to 10 times), the question of interest is added to the preset product database.
  • the server updates the preset product database according to the voice information received from the customer, so that the later product recommendation is more intelligent.
  • This embodiment is a step after step S40 in the first embodiment.
  • the server can score customers according to customer data streams to realize potential customer mining.
  • the voice recognition-based product recommendation method includes :
  • Step S70 When it is detected that the voice information transmission of the terminal is suspended, acquire the customer voice time and customer text data in the customer data stream.
  • the server When the server detects that the terminal's voice information transmission is suspended, that is, when the server detects that a customer communication is completed, the server obtains the customer voice time and customer text data in the customer data stream, where the customer voice time refers to the total time of the customer voice information.
  • Step S80 the customer data stream is scored according to the customer voice time and the customer text data, and when the score is higher than a preset score value, the customer corresponding to the customer data stream is regarded as a target customer and marked.
  • the server scores the customer data stream according to the customer voice time and customer text data.
  • the customer voice time in the customer data stream is more than 2 minutes
  • the customer text data contains: Please introduce the xxx product, the customer data stream is scored 8 -10 points; the customer voice time in the customer data stream is 1 to 2 minutes, the customer data stream score is 4-7 points; the customer voice time in the customer data stream is less than 2 minutes, the customer data stream score is 0-3 points
  • the server sets the customer data stream corresponding to the customer whose score is higher than the preset score value (the preset score value is a preset score value, and the preset score value can be set to 6 points) as target customers and marks them.
  • the data stream identifies and identifies target customers who have a tendency to purchase the corresponding product for later follow-up, making telemarketing more intelligent.
  • an embodiment of the present application also proposes a product recommendation device based on voice recognition, and the product recommendation device based on voice recognition includes:
  • the voice processing module 10 is configured to process the voice information to generate a customer data stream and a sales data stream when the voice information sent by the terminal is received;
  • the detection and analysis module 20 is configured to obtain the time node and customer attention text corresponding to the positive emotional fluctuation when a positive emotional fluctuation is detected in the customer data stream;
  • the retrospective acquisition module 30 is configured to trace the sales data stream according to the time node and the customer attention text, and acquire the target sales text data in the sales data stream that causes the positive mood fluctuations;
  • the obtaining and sending module 40 is used to obtain the product information corresponding to the target sales text data in the preset product database, and send the product information to the terminal, so that the corresponding sales staff of the terminal can introduce the product information according to the product information. .
  • the voice processing module 10 includes:
  • the voice receiving unit is configured to recognize the voice information through a preset voice processing model when receiving the voice information sent by the terminal, obtain the voiceprint characteristics corresponding to the voice information, and divide the voice information according to the voiceprint Features are divided into customer voice information and sales voice information;
  • the first generating unit is configured to recognize the customer voice information through the preset voice processing model to obtain corresponding customer text data and customer voice feature data, and arrange the customer text data and the customer voice feature data in a time sequence Sort and generate customer data stream;
  • the second generating unit is configured to recognize the sales voice information through the preset voice processing model to obtain corresponding sales text data and sales voice feature data, and arrange the sales text data and the sales voice feature data in a time sequence Sort to generate sales data stream.
  • the detection and analysis module 20 includes:
  • the information comparison unit is used to compare the customer text data in the customer data stream with the target words in the preset target vocabulary
  • the comparison and determination unit is configured to determine that there is a positive mood swing in the customer data stream when there is target customer text data that matches the target word;
  • the first acquiring unit is configured to use the target customer text data as customer attention text, and obtain the time node corresponding to the customer attention text in the customer data stream.
  • the customer data stream includes a customer voice feature data stream and a customer text data stream;
  • the detection and analysis module 20 includes:
  • the information analysis unit is used to obtain basic feature data in the customer voice feature data stream, and determine that positive emotions appear in the customer data stream when the customer voice feature data stream is higher than the basic feature data fluctuation;
  • the second acquiring unit is configured to acquire the time node corresponding to the positive mood fluctuation in the customer voice feature data stream, and acquire the customer attention text corresponding to the time node in the customer text data stream.
  • the sales data stream includes a sales text data stream
  • the retrospective acquisition module 30 includes:
  • the information comparison unit is used to obtain the target sales text data stream of the preset time period before the time node in the sales text data stream, and compare the sales text data in the target sales text data stream with the customer attention text Compare
  • the information acquisition unit is used to acquire target sales text data that matches the customer's attention text.
  • the product recommendation device based on voice recognition includes:
  • An acquisition and extraction module configured to acquire customer feedback voice information based on the product information, and extract questions of interest from the customer feedback voice information
  • the statistical update module is used to count the number of questions asked for each question of interest, and when the number of questions asked for the question of interest exceeds a preset threshold, add the question of interest to the preset product database.
  • the product recommendation device based on voice recognition further includes:
  • the detection and acquisition module is configured to acquire the customer voice time and customer text data in the customer data stream when it is detected that the voice information transmission of the terminal is suspended;
  • the evaluation marking module is used to score the customer data stream according to the customer voice time and the customer text data, and when the score is higher than a preset score value, the customer corresponding to the customer data stream is regarded as the target customer and marked .
  • each functional module of the voice recognition-based product recommendation device can refer to the various embodiments of the voice recognition-based product recommendation method of the present application, which will not be repeated here.
  • the embodiment of the present application also proposes a computer storage medium.
  • the computer storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the operations in the voice recognition-based product recommendation method provided in the foregoing embodiments are implemented.

Abstract

La présente invention concerne un procédé de recommandation de produit basé sur la reconnaissance vocale, consistant à : traiter des informations vocales lorsque les informations vocales envoyées par un terminal sont reçues et générer un flux de données de client et un flux de données de vente ; lorsqu'il est détecté qu'une fluctuation d'émotion directe se produit dans le flux de données de client, obtenir un nœud de temps et un texte d'attention de client correspondant à la fluctuation d'émotion directe ; tracer le flux de données de vente en fonction du nœud de temps et du texte d'attention de client et obtenir des données de texte de vente cibles provoquant la fluctuation d'émotion directe dans le flux de données de vente ; obtenir des informations de produit correspondant aux données de texte de vente cibles dans une base de données de produits prédéfinie et envoyer les informations de produit au terminal, de telle sorte qu'un personnel de vente correspondant au terminal puisse présenter des produits selon les informations de produit. La présente invention concerne en outre un appareil et un dispositif de recommandation de produit basés sur une reconnaissance vocale, ainsi qu'un support de stockage. La présente invention améliore la précision de l'analyse de demande de client au moyen d'une analyse soignée du flux de données de client.
PCT/CN2019/121198 2019-06-19 2019-11-27 Procédé, appareil et dispositif de recommandation de produit basés sur une reconnaissance vocale et support de stockage WO2020253115A1 (fr)

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CN201910535455.6 2019-06-19
CN201910535455.6A CN110335596A (zh) 2019-06-19 2019-06-19 基于语音识别的产品推荐方法、装置、设备和存储介质

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