WO2019095586A1 - Procédé de génération de comptes rendus de réunions, serveur d'application et support d'informations lisible par ordinateur - Google Patents

Procédé de génération de comptes rendus de réunions, serveur d'application et support d'informations lisible par ordinateur Download PDF

Info

Publication number
WO2019095586A1
WO2019095586A1 PCT/CN2018/077628 CN2018077628W WO2019095586A1 WO 2019095586 A1 WO2019095586 A1 WO 2019095586A1 CN 2018077628 W CN2018077628 W CN 2018077628W WO 2019095586 A1 WO2019095586 A1 WO 2019095586A1
Authority
WO
WIPO (PCT)
Prior art keywords
speaker
content
speakers
meeting
meeting minutes
Prior art date
Application number
PCT/CN2018/077628
Other languages
English (en)
Chinese (zh)
Inventor
王健宗
黄章成
程宁
肖京
Original Assignee
平安科技（深圳）有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 平安科技（深圳）有限公司 filed Critical 平安科技（深圳）有限公司
Publication of WO2019095586A1 publication Critical patent/WO2019095586A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/60—Information retrieval; Database structures therefor; File system structures therefor of audio data
    • G06F16/61—Indexing; Data structures therefor; Storage structures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/10—Text processing
    • G06F40/166—Editing, e.g. inserting or deleting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/30—Semantic analysis
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/02—Feature extraction for speech recognition; Selection of recognition unit
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/26—Speech to text systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04M—TELEPHONIC COMMUNICATION
    • H04M3/00—Automatic or semi-automatic exchanges
    • H04M3/42—Systems providing special services or facilities to subscribers
    • H04M3/56—Arrangements for connecting several subscribers to a common circuit, i.e. affording conference facilities

Definitions

  • the present application relates to the field of voice processing technologies, and in particular, to a conference minutes generation method, an application server, and a computer readable storage medium.
  • the present application provides a method for generating a meeting minutes, an application server, and a computer readable storage medium, which can automatically summarize and generate meeting minutes according to meeting content records, thereby saving human resource costs.
  • the present application provides an application server, where the application server includes a memory, a processor, and a memory meeting generation system that can be run on the processor, where the meeting minutes are generated.
  • the system is executed by the processor, the following steps are performed: acquiring audio record information of a conference, and extracting, from the audio record information, the content of each speaker according to the voice feature of each speaker; The content of the speech of the speaker is subjected to keyword extraction; and the meeting minutes corresponding to the meeting are generated according to the extracted keywords.
  • the present application further provides a method for generating a meeting minutes, which is applied to an application server, the method comprising: acquiring audio record information of a conference, and recording the audio record according to the voice feature of each speaker. Extracting the content of each of the speakers of the information; performing keyword extraction on the content of the speech of each of the speakers; and generating a meeting minutes corresponding to the meeting according to the extracted keywords.
  • the present application further provides a computer readable storage medium storing a meeting minutes generating system, the meeting minutes generating system being executable by at least one processor, so that The at least one processor performs the steps of the method of generating a meeting minutes as described above.
  • the conference minutes generating method, the application server, and the computer readable storage medium proposed by the present application first acquire audio recording information of a conference, and from the audio recording according to the voice characteristics of each speaker.
  • the content of each speaker of the speaker is extracted from the information; secondly, keyword extraction is performed on the content of the speech of each of the speakers; and finally, the meeting minutes corresponding to the meeting are generated according to the extracted keywords.
  • the participants in the meeting can focus more on the content and process of the meeting.
  • the meeting summary is streamlined and accurate. It can also be used for reference and reference by other people in need. Compared with traditional manual recording, this solution is more efficient and accurate, and saves human resource costs.
  • FIG. 1 is a schematic diagram of an optional application environment of each embodiment of the present application.
  • FIG. 2 is a schematic diagram of an optional hardware architecture of an application server of the present application
  • FIG. 3 is a schematic diagram of a program module of a first embodiment of a meeting minutes generation system of the present application
  • FIG. 4 is a schematic diagram of a program module of a second embodiment of the meeting minutes generating system of the present application.
  • FIG. 5 is a schematic flowchart of an implementation process of a first embodiment of a method for generating a meeting minutes of the present application
  • FIG. 6 is a schematic diagram of an implementation process of a second embodiment of a method for generating a meeting minutes of the present application.
  • FIG. 1 it is a schematic diagram of an optional application environment of each embodiment of the present application.
  • the present application is applicable to an application environment including, but not limited to, the terminal device 1, the application server 2, and the network 3.
  • the terminal device 1 may be a mobile phone, a smart phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a navigation device, an in-vehicle device, etc. Mobile devices, etc., as well as fixed terminals such as digital TVs, desktop computers, notebooks, broadband phones, servers, and the like.
  • the application server 2 may be a computing device such as a rack server, a blade server, a tower server, or a rack server.
  • the application server 2 may be a standalone server or a server cluster composed of multiple servers.
  • the network 3 may be an intranet, an Internet, a Global System of Mobile communication (GSM), a Wideband Code Division Multiple Access (WCDMA), a 4G network, Wireless or wired networks such as 5G networks, Bluetooth, Wi-Fi, and call
  • the application server 2 can be respectively connected to one or more of the terminal devices 1 through the network 3 for data transmission and interaction.
  • FIG. 2 it is a schematic diagram of an optional hardware architecture of the application server 2 of the present application.
  • the application server 2 may include, but is not limited to, the memory 11, the processor 12, and the network interface 13 being communicably connected to each other through a system bus. It is to be noted that FIG. 2 only shows the application server 2 with components 11-13, but it should be understood that not all illustrated components may be implemented, and more or fewer components may be implemented instead.
  • the memory 11 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (eg, SD or DX memory, etc.), a random access memory (RAM), a static Random access memory (SRAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), magnetic memory, magnetic disk, optical disk, and the like.
  • the memory 11 may be an internal storage unit of the application server 2, such as a hard disk or memory of the application server 2.
  • the memory 11 may also be an external storage device of the application server 2, such as a plug-in hard disk equipped on the application server 2, a smart memory card (SMC), and a secure digital number. (Secure Digital, SD) card, flash card, etc.
  • the memory 11 can also include both the internal storage unit of the application server 2 and its external storage device.
  • the memory 11 is generally used to store an operating system installed in the application server 2 and various types of application software, such as program code of the meeting minutes generation system 100. Further, the memory 11 can also be used to temporarily store various types of data that have been output or are to be output.
  • the processor 12 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments.
  • the processor 12 is typically used to control the overall operation of the application server 2, such as performing control and processing related to data interaction or communication with the terminal device 1.
  • the processor 12 is configured to run program code or process data stored in the memory 11, such as running the conference minutes generating system and the like.
  • the network interface 13 may comprise a wireless network interface or a wired network interface, which is typically used to establish a communication connection between the application server 2 and other electronic devices.
  • the network interface 13 is mainly used to connect the application server 2 to one or more of the terminal devices 1 through the network 3, and the application server 2 and the one or more terminals. A data transmission channel and a communication connection are established between the devices 1.
  • the present application proposes a meeting minutes generation system 100.
  • FIG. 3 it is a program module diagram of the first embodiment of the meeting minutes generation system 100 of the present application.
  • the meeting minutes generating system 100 includes a series of computer program instructions stored in the memory 11, and when the computer program instructions are executed by the processor 12, the meeting minutes generating operation of the embodiments of the present application can be implemented. .
  • the meeting minutes generation system 100 can be divided into one or more modules based on the particular operations implemented by the various portions of the computer program instructions. For example, in FIG. 3, the meeting minutes generation system 100 can be divided into a content acquisition module 101, an extraction module 102, and a generation module 103. among them:
  • the content obtaining module 101 is configured to obtain audio record information of a conference, and extract, from the audio record information, the content of each speaker's speech according to the voice feature of each speaker.
  • the application server 2 collects the conference voice content through each terminal device 1, receives the voice content sent by each terminal device 1 and saves the voice content, and the voice content can be saved into a specified audio format, such as MP3. , wma, wav, etc.
  • the terminal device 1 collects the voice content through a sound collecting device (for example, a microphone).
  • the terminal device 1 can send the collected voice content to the application server 2 in real time or periodically, or when the participant on the side of the terminal device 1 ends a speech, the terminal device 1 will continuously collect the voice.
  • the content is sent to the application server 2.
  • the application server 2 After receiving the voice content sent by the terminal device 1, the application server 2 saves the voice content.
  • the content obtaining module 101 can obtain the audio record information of the conference, because the full voice content of the conference is saved on the application server 2.
  • the audio recording information is preferably the voice content of the conference.
  • the conference call is a video conference call
  • the conference record received and saved by the application server 2 is audio and video (voice and video screen) content, and at this time, the audio record information acquired by the content acquisition module 101 Also preferred is the voice content of the conference.
  • the voice characteristics of each speaker can be pre-acquired prior to the meeting. Specifically, each participant is preset with a unique ID number. Before the meeting, the voice characteristics of each participant are pre-admitted, and then an identity index table is established according to the voice characteristics and ID number of each participant. The identity index table stores the correspondence between the voice characteristics of each participant and the ID of each participant, thereby enabling confirmation of the membership of the participant.
  • the participants can be from the local or remote speakers.
  • the speaker's voice feature may be generated into a speaker model, and the speaker model and the corresponding speaker ID number are stored in the identity index table.
  • the speaker sound feature of the segment of the voice content needs to be extracted first, and the sound feature is extracted. Compare with each speaker model in the identity index table and get a matching score. If the matching score reaches a preset score, it indicates that the speaker model corresponding to the sound feature parameter exists in the index table, thereby obtaining the speaker ID number and confirming the speaker identity. Otherwise, it indicates that there is no speaker model corresponding to the sound feature in the index table, and a new speaker model and a new ID number are generated according to the sound feature, and stored in the identity index table, so as to facilitate the search for matching.
  • a UBM model general background model
  • an i-vector extraction algorithm can be used for matching scoring.
  • the i-vector value is calculated from the two pieces of speech content as the sound characteristics of the speaker of the two pieces of speech content.
  • the input is scored by the dot-product algorithm or the PLDA algorithm. If the score exceeds a certain threshold, it is considered that the two speech contents belong to the same speaker. .
  • the content acquisition module 101 may extract each audio from the audio record information according to the voice feature of each speaker. The content of a speech by the speaker.
  • the extracting module 102 is configured to perform keyword extraction on the content of the speech of each of the speakers.
  • the voice content of each speaker may be converted into a corresponding text before keyword extraction.
  • the extraction module 102 may first sort the multiple segments of text content in a certain order. For example, the multi-segment text content can be sorted according to the time axis (eg, according to the order in which the text content is generated, the number of sentences, the serial number, etc.).
  • the extraction module 102 can employ a TF-IDF algorithm to extract keywords for each of the speakers' speech content.
  • the TF-IDF algorithm can be used to assess how important a word is in a spoken text. The importance of a word increases proportionally with the number of times it appears in the text.
  • the TF-IDF value of a certain word is obtained by word frequency (TF) and inverse document frequency (IDF), and the TF-IDF value is higher if the word is more important to the spoken text.
  • TF word frequency
  • IDF inverse document frequency
  • the extraction module 102 can rank the TF-IDF value in the first few words as the keyword of the utterance text. For example, a word with the TF-IDF value ranked in the top five is used as a keyword for the spoken text.
  • the generating module 103 is configured to generate a meeting minutes corresponding to the meeting according to the extracted keywords.
  • the generating module 103 may generate a meeting minutes based on the extracted keywords in combination with the speaking content to which each keyword belongs. In other implementation manners of the present application, the generating module 103 may further take the speaker's intonation (generally, the higher the intonation of the voice content, correspondingly, the higher the importance of the voice content) as a consideration parameter to generate The meeting minutes.
  • the generating module 103 may further process the generated meeting minutes by using an NLP natural language algorithm to generate a more fluent and standardized meeting minutes.
  • the NLP analysis engine based on the NLP natural language algorithm can pre-collect and store a large amount of real corpus, so that the linguistic behavior of the words in the meeting minutes can be revised.
  • the meeting minutes generating system 100 includes a series of computer program instructions stored in the memory 11, and when the computer program instructions are executed by the processor 12, the meeting minutes generating operation of the embodiments of the present application can be implemented. .
  • the meeting minutes generation system 100 can be divided into one or more modules based on the particular operations implemented by the various portions of the computer program instructions.
  • the meeting minutes generation system 100 can be divided into a content acquisition module 101, an extraction module 102, a generation module 103, a feature creation module 104, and a transmission module 105.
  • the program modules 101-103 are the same as the first embodiment of the meeting minutes generation system 100 of the present application, and the feature creation module 104 and the transmission module 105 are added thereto. among them:
  • the feature establishing module 104 is configured to acquire a voice sample of each of the speakers, and extract a sound feature of each of the speakers from a voice sample of each of the speakers.
  • each participant is required to perform a conference check-in by voice to obtain a voice sample, thereby realizing pre-admission of the voice of each participant and performing sound feature extraction.
  • the sending module 105 is configured to send the meeting minutes generated by the generating module 103 to the preset user by mail or fax, or provide a link to the preset user to obtain the meeting minutes.
  • the preset user may be a participant or other pre-designated person.
  • the sending module 105 may also encrypt the meeting minutes to ensure data security before storing or transmitting the meeting minutes.
  • the meeting minutes are compressed and encrypted, and the decompression password is a designated password or a password known or agreed by each participant.
  • the present application also proposes a method for generating a meeting minutes.
  • FIG. 5 it is a schematic flow chart of the implementation of the first embodiment of the method for generating meeting minutes of the present application.
  • the order of execution of the steps in the flowchart shown in FIG. 5 may be changed according to different requirements, and some steps may be omitted.
  • Step S502 Acquire audio record information of a conference, and extract the content of each speaker's speech from the audio record information according to the voice feature of each speaker.
  • the application server 2 collects the conference voice content through each terminal device 1, receives the voice content sent by each terminal device 1 and saves the voice content, and the voice content can be saved into a specified audio format, such as MP3. , wma, wav, etc.
  • the terminal device 1 collects the voice content through a sound collection device (for example, a microphone).
  • the terminal device 1 can send the collected voice content to the application server 2 in real time or periodically, or when the participant on the side of the terminal device 1 ends a speech, the terminal device 1 will continuously collect the voice.
  • the content is sent to the application server 2.
  • the application server 2 After receiving the voice content sent by the terminal device 1, the application server 2 saves the voice content.
  • the audio record information of the conference can be obtained from the application server 2.
  • the audio recording information is preferably the voice content of the conference.
  • the conference call is a video conference call
  • the conference record received and saved by the application server 2 is audio and video (voice and video picture) content, and at this time, the acquired audio record information is also preferably the same.
  • the voice content of the meeting is a video conference call.
  • the voice characteristics of each speaker can be pre-acquired prior to the meeting. Specifically, each participant is preset with a unique ID number. Before the meeting, the voice characteristics of each participant are pre-admitted, and then an identity index table is established according to the voice characteristics and ID number of each participant. The identity index table stores the correspondence between the voice characteristics of each participant and the ID of each participant, thereby enabling confirmation of the membership of the participant.
  • the participants can be from the local or remote speakers.
  • the speaker's voice feature may be generated into a speaker model, and the speaker model and the corresponding speaker ID number are stored in the identity index table.
  • the speaker sound feature of the segment of the voice content needs to be extracted first, and the sound feature is extracted. Compare with each speaker model in the identity index table and get a matching score. If the matching score reaches a preset score, it indicates that the speaker model corresponding to the sound feature parameter exists in the index table, thereby obtaining the speaker ID number and confirming the speaker identity. Otherwise, it indicates that there is no speaker model corresponding to the sound feature in the index table, and a new speaker model and a new ID number are generated according to the sound feature, and stored in the identity index table, so as to facilitate the search for matching.
  • a UBM model general background model
  • an i-vector extraction algorithm can be used for matching scoring.
  • the i-vector value is calculated from the two pieces of speech content as the sound characteristics of the speaker of the two pieces of speech content.
  • the input is scored by the dot-product algorithm or the PLDA algorithm. If the score exceeds a certain threshold, it is considered that the two speech contents belong to the same speaker. .
  • the voice of each speaker can be extracted from the audio record information according to the voice feature of each speaker.
  • the content of the speech is
  • Step S504 performing keyword extraction on the content of the speech of each of the speakers.
  • the voice content of each speaker may be converted into a corresponding text before keyword extraction.
  • the plurality of pieces of text content may be first sorted in a certain order.
  • the multi-segment text content can be sorted according to the time axis (eg, according to the order in which the text content is generated, the number of sentences, the serial number, etc.).
  • a TF-IDF algorithm may be employed to extract keywords for each of the speakers' speech content.
  • the TF-IDF algorithm can be used to assess how important a word is in a spoken text. The importance of a word increases proportionally with the number of times it appears in the text.
  • the TF-IDF value of a certain word is obtained by word frequency (TF) and inverse document frequency (IDF), and the TF-IDF value is higher if the word is more important to the spoken text. The bigger. Therefore, the first few words of the TF-IDF value can be used as the keywords of the speech text. For example, a word with the TF-IDF value ranked in the top five is used as a keyword for the spoken text.
  • Step S506 generating a meeting minutes corresponding to the meeting according to the extracted keywords.
  • the meeting minutes may be generated based on the extracted keywords in combination with the speaking content to which each keyword belongs.
  • the speaker's intonation (generally, the higher the intonation of the voice content, correspondingly, the higher the importance of the voice content) may be further taken as a consideration parameter to generate the conference. summary.
  • the generated meeting minutes may be further processed by an NLP natural language algorithm to generate a more fluent and standardized meeting minutes.
  • the NLP analysis engine based on the NLP natural language algorithm can pre-collect and store a large amount of real corpus, so that the linguistic behavior of the words in the meeting minutes can be revised.
  • the conference minutes generating method proposed by the present application firstly acquires audio record information of the conference, and extracts each of the speakers from the audio record information according to the voice feature of each speaker.
  • the content of the speech secondly, performing keyword extraction on the content of the speech of each of the speakers; further, generating a meeting minutes corresponding to the meeting according to the extracted keywords; and finally, generating the meeting minutes by mail Or send it to the preset user in the form of a fax, or provide a link to the preset user to obtain the meeting minutes.
  • the participants in the meeting can focus more on the content and process of the meeting.
  • the meeting summary is streamlined and accurate. It can also be used for reference and reference by other people in need. Compared with traditional manual recording, this solution is more efficient and accurate, and saves human resource costs.
  • FIG. 6 it is a schematic diagram of an implementation process of a second embodiment of a method for generating a meeting minutes of the present application.
  • the order of execution of the steps in the flowchart shown in FIG. 6 may be changed according to different requirements, and some steps may be omitted.
  • Step S500 Acquire a voice sample of each of the speakers, and extract a sound feature of each of the speakers from a voice sample of each of the speakers.
  • each participant is required to perform a conference check-in by voice to obtain a voice sample, thereby realizing pre-admission of the voice of each participant and performing sound feature extraction.
  • Step S502 Acquire audio record information of a conference, and extract the content of each speaker's speech from the audio record information according to the voice feature of each speaker.
  • the application server 2 collects the conference voice content through each terminal device 1, receives the voice content sent by each terminal device 1 and saves the voice content, and the voice content can be saved into a specified audio format, such as MP3. , wma, wav, etc.
  • the terminal device 1 collects the voice content through a sound collection device (for example, a microphone).
  • the terminal device 1 can send the collected voice content to the application server 2 in real time or periodically, or when the participant on the side of the terminal device 1 ends a speech, the terminal device 1 will continuously collect the voice.
  • the content is sent to the application server 2.
  • the application server 2 After receiving the voice content sent by the terminal device 1, the application server 2 saves the voice content.
  • the audio record information of the conference can be obtained from the application server 2.
  • the audio recording information is preferably the voice content of the conference.
  • the conference call is a video conference call
  • the conference record received and saved by the application server 2 is audio and video (voice and video picture) content, and at this time, the acquired audio record information is also preferably the same.
  • the voice content of the meeting is a video conference call.
  • the voice characteristics of each speaker can be pre-acquired prior to the meeting. Specifically, each participant is preset with a unique ID number. Before the meeting, the voice characteristics of each participant are pre-admitted, and then an identity index table is established according to the voice characteristics and ID number of each participant. The identity index table stores the correspondence between the voice characteristics of each participant and the ID of each participant, thereby enabling confirmation of the membership of the participant.
  • the participants can be from the local or remote speakers.
  • the speaker's voice characteristics may be generated into a speaker model, and the speaker model and the corresponding speaker ID number are stored in the identity index table.
  • the speaker sound feature of the segment of the voice content needs to be extracted first, and the sound feature is extracted. Compare with each speaker model in the identity index table and get a matching score. If the matching score reaches a preset score, it indicates that the speaker model corresponding to the sound feature parameter exists in the index table, thereby obtaining the speaker ID number and confirming the speaker identity. Otherwise, it indicates that there is no speaker model corresponding to the sound feature in the index table, and a new speaker model and a new ID number are generated according to the sound feature, and stored in the identity index table, so as to facilitate the search for matching.
  • a UBM model general background model
  • an i-vector extraction algorithm can be used for matching scoring.
  • the i-vector value is calculated from the two pieces of speech content as the sound characteristics of the speaker of the two pieces of speech content.
  • the input is scored by the dot-product algorithm or the PLDA algorithm. If the score exceeds a certain threshold, it is considered that the two speech contents belong to the same speaker. .
  • the voice of each speaker can be extracted from the audio record information according to the voice feature of each speaker.
  • the content of the speech is
  • Step S504 performing keyword extraction on the content of the speech of each of the speakers.
  • the voice content of each speaker may be converted into a corresponding text before keyword extraction.
  • the plurality of pieces of text content may be first sorted in a certain order.
  • the multi-segment text content can be sorted according to the time axis (eg, according to the order in which the text content is generated, the number of sentences, the serial number, etc.).
  • a TF-IDF algorithm may be employed to extract keywords for each of the speakers' speech content.
  • the TF-IDF algorithm can be used to assess how important a word is in a spoken text. The importance of a word increases proportionally with the number of times it appears in the text.
  • the TF-IDF value of a certain word is obtained by word frequency (TF) and inverse document frequency (IDF), and the TF-IDF value is higher if the word is more important to the spoken text. The bigger. Therefore, the first few words of the TF-IDF value can be used as the keywords of the speech text. For example, a word with the TF-IDF value ranked in the top five is used as a keyword for the spoken text.
  • Step S506 generating a meeting minutes corresponding to the meeting according to the extracted keywords.
  • the meeting minutes may be generated based on the extracted keywords in combination with the speaking content to which each keyword belongs.
  • the speaker's intonation (generally, the higher the intonation of the voice content, correspondingly, the higher the importance of the voice content) may be further taken as a consideration parameter to generate the conference. summary.
  • the generated meeting minutes may be further processed by an NLP natural language algorithm to generate a more fluent and standardized meeting minutes.
  • the NLP analysis engine based on the NLP natural language algorithm can pre-collect and store a large amount of real corpus, so that the linguistic behavior of the words in the meeting minutes can be revised.
  • Step S508 sending the meeting minutes to the preset user by mail or fax, or providing a link to the preset user to obtain the meeting minutes.
  • the preset user may be a participant or other pre-designated person.
  • the meeting minutes may also be encrypted prior to storing or transmitting the meeting minutes to ensure data security. For example, compress and encrypt the meeting minutes, decompress the password as a specified password or a password known or agreed by each participant.
  • the method for generating meeting minutes proposed by the present application firstly acquires a voice sample of each of the speakers, and extracts each of the speakers from the voice samples of each of the speakers. a sound feature; secondly, acquiring audio record information of the conference, and extracting the content of each speaker from the audio record information according to the voice feature of each speaker; and, for each of the speakers
  • the content of the speech of the person is extracted by the keyword; further, the meeting minutes corresponding to the meeting are generated according to the extracted keywords; finally, the generated meeting minutes are sent to the preset user by mail or fax, or The preset user provides a link to obtain the meeting minutes.
  • the foregoing embodiment method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be through hardware, but in many cases, the former is better.
  • Implementation Based on such understanding, the technical solution of the present application, which is essential or contributes to the prior art, may be embodied in the form of a software product stored in a storage medium (such as ROM/RAM, disk,
  • the optical disc includes a number of instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the methods described in various embodiments of the present application.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Computational Linguistics (AREA)
  • Health & Medical Sciences (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Multimedia (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Acoustics & Sound (AREA)
  • Human Computer Interaction (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Software Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • Signal Processing (AREA)
  • Telephonic Communication Services (AREA)
  • Toys (AREA)

Abstract

La présente invention concerne un procédé de génération de comptes rendus de réunions. Le procédé comprend les étapes consistant : à obtenir des informations d'enregistrement audio d'une réunion, et à extraire un contenu de parole de chaque locuteur des informations d'enregistrement audio en fonction des caractéristiques sonores de chaque locuteur; à extraire un mot-clé du contenu de parole de chaque locuteur; et à générer des comptes rendus de réunions correspondant à la réunion en fonction des mots-clés extraits. La présente invention concerne également un serveur d'application et un support d'informations lisible par ordinateur. Grâce au procédé de génération de comptes rendus de réunions, au serveur d'application et au support d'informations lisible par ordinateur selon la présente invention, des comptes rendus de réunions peuvent être automatiquement résumés et générés sur la base d'enregistrements de contenu de réunion, ce qui permet de réduire les coûts des ressources humaines.
PCT/CN2018/077628 2017-11-17 2018-02-28 Procédé de génération de comptes rendus de réunions, serveur d'application et support d'informations lisible par ordinateur WO2019095586A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201711141751.5A CN108022583A (zh) 2017-11-17 2017-11-17 会议纪要生成方法、应用服务器及计算机可读存储介质
CN201711141751.5 2017-11-17

Publications (1)

Publication Number Publication Date
WO2019095586A1 true WO2019095586A1 (fr) 2019-05-23

Family

ID=62080675

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2018/077628 WO2019095586A1 (fr) 2017-11-17 2018-02-28 Procédé de génération de comptes rendus de réunions, serveur d'application et support d'informations lisible par ordinateur

Country Status (2)

Country Link
CN (1) CN108022583A (fr)
WO (1) WO2019095586A1 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113014540A (zh) * 2020-11-24 2021-06-22 腾讯科技（深圳）有限公司 一种数据处理方法、装置、设备及存储介质

Families Citing this family (22)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109525800A (zh) * 2018-11-08 2019-03-26 江西国泰利民信息科技有限公司 一种远程会议语音识别数据传输方法
CN109361825A (zh) * 2018-11-12 2019-02-19 平安科技（深圳）有限公司 会议纪要记录方法、终端及计算机存储介质
CN109473103A (zh) * 2018-11-16 2019-03-15 上海玖悦数码科技有限公司 一种会议纪要生成方法
CN109543173A (zh) * 2018-11-30 2019-03-29 苏州麦迪斯顿医疗科技股份有限公司 抢救记录生成方法、装置、电子设备及存储介质
CN109803059A (zh) * 2018-12-17 2019-05-24 百度在线网络技术（北京）有限公司 音频处理方法和装置
CN111415128B (zh) * 2019-01-07 2024-06-07 阿里巴巴集团控股有限公司 控制会议的方法、系统、装置、设备和介质
CN109960743A (zh) * 2019-01-16 2019-07-02 平安科技（深圳）有限公司 会议内容区分方法、装置、计算机设备及存储介质
CN110049270B (zh) * 2019-03-12 2023-05-30 平安科技（深圳）有限公司 多人会议语音转写方法、装置、系统、设备及存储介质
CN110010130A (zh) * 2019-04-03 2019-07-12 安徽阔声科技有限公司 一种面向参会者同步语音转写文字的智能方法
CN110134756A (zh) * 2019-04-15 2019-08-16 深圳壹账通智能科技有限公司 会议记录生成方法、电子装置及存储介质
CN110298252A (zh) * 2019-05-30 2019-10-01 平安科技（深圳）有限公司 会议纪要生成方法、装置、计算机设备及存储介质
CN110322872A (zh) * 2019-06-05 2019-10-11 平安科技（深圳）有限公司 会议语音数据处理方法、装置、计算机设备和存储介质
CN111277589A (zh) * 2020-01-19 2020-06-12 腾讯云计算（北京）有限责任公司 会议文档生成方法及装置
CN111626061A (zh) * 2020-05-27 2020-09-04 深圳前海微众银行股份有限公司 会议记录生成方法、装置、设备及可读存储介质
CN111666746B (zh) * 2020-06-05 2023-09-29 中国银行股份有限公司 会议纪要的生成方法及装置、电子设备及存储介质
CN113782026A (zh) * 2020-06-09 2021-12-10 北京声智科技有限公司 一种信息处理方法、装置、介质和设备
CN111787172A (zh) * 2020-06-12 2020-10-16 深圳市珍爱捷云信息技术有限公司 基于移动终端实现电话会议方法、装置、服务器和存储介质
CN111797226B (zh) * 2020-06-30 2024-04-05 北京百度网讯科技有限公司 会议纪要的生成方法、装置、电子设备以及可读存储介质
CN111899742B (zh) * 2020-08-06 2021-03-23 广州科天视畅信息科技有限公司 一种提高会议进行效率的方法及系统
CN112687272B (zh) * 2020-12-18 2023-03-21 北京金山云网络技术有限公司 一种会议纪要的记录方法、装置及电子设备
CN113766170A (zh) * 2021-09-18 2021-12-07 苏州科天视创信息科技有限公司 基于音视频的在线会议多端资源共享方法及系统
CN114757155B (zh) * 2022-06-14 2022-09-27 深圳乐播科技有限公司 一种会议文档的生成方法及装置

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102572372A (zh) * 2011-12-28 2012-07-11 中兴通讯股份有限公司 会议纪要的提取方法和装置
CN104427292A (zh) * 2013-08-22 2015-03-18 中兴通讯股份有限公司 会议纪要的提取方法及装置
US20150348538A1 (en) * 2013-03-14 2015-12-03 Aliphcom Speech summary and action item generation
CN106448675A (zh) * 2016-10-21 2017-02-22 科大讯飞股份有限公司 识别文本修正方法及系统
CN106802885A (zh) * 2016-12-06 2017-06-06 乐视控股（北京）有限公司 一种会议纪要自动记录方法、装置和电子设备

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9560206B2 (en) * 2010-04-30 2017-01-31 American Teleconferencing Services, Ltd. Real-time speech-to-text conversion in an audio conference session
CN105957531B (zh) * 2016-04-25 2019-12-31 上海交通大学 基于云平台的演讲内容提取方法及装置

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102572372A (zh) * 2011-12-28 2012-07-11 中兴通讯股份有限公司 会议纪要的提取方法和装置
US20150348538A1 (en) * 2013-03-14 2015-12-03 Aliphcom Speech summary and action item generation
CN104427292A (zh) * 2013-08-22 2015-03-18 中兴通讯股份有限公司 会议纪要的提取方法及装置
CN106448675A (zh) * 2016-10-21 2017-02-22 科大讯飞股份有限公司 识别文本修正方法及系统
CN106802885A (zh) * 2016-12-06 2017-06-06 乐视控股（北京）有限公司 一种会议纪要自动记录方法、装置和电子设备

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113014540A (zh) * 2020-11-24 2021-06-22 腾讯科技（深圳）有限公司 一种数据处理方法、装置、设备及存储介质
CN113014540B (zh) * 2020-11-24 2022-09-27 腾讯科技（深圳）有限公司 一种数据处理方法、装置、设备及存储介质

Also Published As

Publication number Publication date
CN108022583A (zh) 2018-05-11

Similar Documents

Publication Publication Date Title
WO2019095586A1 (fr) Procédé de génération de comptes rendus de réunions, serveur d'application et support d'informations lisible par ordinateur
US10958598B2 (en) Method and apparatus for generating candidate reply message
CN103187053B (zh) 输入方法和电子设备
CN111666746B (zh) 会议纪要的生成方法及装置、电子设备及存储介质
CN109388701A (zh) 会议记录生成方法、装置、设备和计算机存储介质
CN104346480B (zh) 信息挖掘方法和装置
US10846330B2 (en) System and methods for vocal commenting on selected web pages
CN110866110A (zh) 基于人工智能的会议纪要生成方法、装置、设备及介质
US20150066935A1 (en) Crowdsourcing and consolidating user notes taken in a virtual meeting
CN109657181B (zh) 互联网信息链式存储方法、装置、计算机设备及存储介质
CN104158945A (zh) 通话信息获取方法、装置及系统
WO2019148585A1 (fr) Procédé et appareil de génération de résumé de conférence
CN109582906B (zh) 数据可靠度的确定方法、装置、设备和存储介质
CN106713111B (zh) 一种添加好友的处理方法、终端及服务器
CN110750619B (zh) 聊天记录关键词的提取方法、装置、计算机设备及存储介质
CN108846098B (zh) 一种信息流摘要生成及展示方法
CN111798118B (zh) 企业经营风险监控方法及装置
WO2020103447A1 (fr) Procédé et appareil de stockage de type à liaison pour les informations vidéo, dispositif informatique et support d'enregistrement
CN112446622A (zh) 企业微信会话评价方法、系统、电子设备及存储介质
KR102030551B1 (ko) 인스턴트 메신저 구동 장치 및 그 동작 방법
CN110705235A (zh) 业务办理的信息录入方法、装置、存储介质及电子设备
CN112395391A (zh) 概念图谱构建方法、装置、计算机设备及存储介质
CN111223487A (zh) 一种信息处理方法及电子设备
CN113111658B (zh) 校验信息的方法、装置、设备和存储介质
CN113741864A (zh) 基于自然语言处理的语义化服务接口自动设计方法与系统

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 18878912

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 02.10.2020)

122 Ep: pct application non-entry in european phase

Ref document number: 18878912

Country of ref document: EP

Kind code of ref document: A1