WO2018121275A1 - Procédé et appareil de connexion d'erreur de reconnaissance vocale dans un dispositif matériel intelligent - Google Patents

Procédé et appareil de connexion d'erreur de reconnaissance vocale dans un dispositif matériel intelligent Download PDF

Info

Publication number
WO2018121275A1
WO2018121275A1 PCT/CN2017/116165 CN2017116165W WO2018121275A1 WO 2018121275 A1 WO2018121275 A1 WO 2018121275A1 CN 2017116165 W CN2017116165 W CN 2017116165W WO 2018121275 A1 WO2018121275 A1 WO 2018121275A1
Authority
WO
WIPO (PCT)
Prior art keywords
keyword
candidate
keywords
score
text information
Prior art date
Application number
PCT/CN2017/116165
Other languages
English (en)
Chinese (zh)
Inventor
杨英
张倩倩
Original Assignee
北京奇虎科技有限公司
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 北京奇虎科技有限公司 filed Critical 北京奇虎科技有限公司
Publication of WO2018121275A1 publication Critical patent/WO2018121275A1/fr

Links

Images

Classifications

    • 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
    • G10L15/08—Speech classification or search
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/08—Speech classification or search
    • G10L15/10—Speech classification or search using distance or distortion measures between unknown speech and reference templates
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/08—Speech classification or search
    • G10L15/18—Speech classification or search using natural language modelling
    • G10L15/1815—Semantic context, e.g. disambiguation of the recognition hypotheses based on word meaning
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/08—Speech classification or search
    • G10L15/18—Speech classification or search using natural language modelling
    • G10L15/1822—Parsing for meaning understanding
    • 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
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/08—Speech classification or search
    • G10L2015/086—Recognition of spelled words
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/08—Speech classification or search
    • G10L2015/088—Word spotting

Definitions

  • the present invention relates to the field of voice recognition technology, and in particular, to a voice recognition error correction method and apparatus in an intelligent hardware device.
  • the process of semantic analysis relies on the accuracy of speech recognition, and the accuracy of speech recognition is difficult to reach 100%. For example, when the user is a child, the unclearness of the speech makes various errors in speech recognition.
  • the present invention has been made in order to provide a speech recognition error correction method and apparatus in an intelligent hardware device that overcomes the above problems or at least partially solves the above problems.
  • a voice recognition error correction method in an intelligent hardware device including:
  • the error correction processing is performed on the keywords in the text information according to the selected one or more candidate words.
  • a speech recognition error correction apparatus in an intelligent hardware device including:
  • a voice recognition unit configured to convert a voice signal received by the smart hardware device into text information by using a voice recognition technology
  • a keyword extracting unit adapted to extract a keyword from the text information
  • a matching unit configured to match the extracted keyword with a vocabulary related to the intelligent hardware service, and select one or more candidate words matching the keyword from the vocabulary;
  • the error correction unit is adapted to perform error correction processing on the keywords in the text information according to the selected one or more candidate words.
  • a computer program comprising computer readable code, when said computer readable code is run on a computing device, causing said computing device to perform an intelligent hardware device as described above Speech recognition error correction method.
  • a computer readable medium storing a computer program as described above is provided.
  • the technical solution of the present invention first uses voice recognition technology to perform voice recognition on a voice signal received by an intelligent hardware device, converts it into text information, and further analyzes the text information to obtain a plurality of keywords, and these The keywords are matched by a vocabulary related to the intelligent hardware service, one or more candidate words are determined, and finally the keywords are corrected using the obtained candidate words.
  • the technical solution fully considers the functional characteristics of the intelligent hardware, and uses the preset business-related vocabulary to intelligently correct the keywords parsed in the speech recognition result, which significantly improves the accuracy of the speech recognition and consumes less resources. In line with the low energy consumption requirements of intelligent hardware devices.
  • FIG. 1 is a flow chart showing a voice recognition error correction method in an intelligent hardware device according to an embodiment of the present invention
  • FIG. 2 is a schematic structural diagram of a speech recognition error correction apparatus in an intelligent hardware device according to an embodiment of the present invention
  • Figure 3 schematically shows a block diagram of a computing device for performing the method according to the invention
  • Fig. 4 schematically shows a storage unit for holding or carrying program code implementing the method according to the invention.
  • FIG. 1 is a schematic flowchart of a voice recognition error correction method in an intelligent hardware device according to an embodiment of the present invention. As shown in FIG. 1, the method includes:
  • Step S110 Convert the voice signal received by the smart hardware device into text information by using a voice recognition technology.
  • the smart hardware device can be a smart phone, a smart watch, an intelligent robot, and the like.
  • Speech recognition technology is a technology that is gradually being improved and put into use.
  • the Siri function in Apple's mobile phone can realize the voice recognition of the user's voice to perform functions such as opening the camera and map navigation in the Apple mobile phone.
  • step S120 keywords are extracted from the text information.
  • the user wants to use the storytelling function in the children's watch to send out the voice signal "I want to hear the story - Little Red Riding Hood", then the "story” is the keyword corresponding to the function, and “Little Red Riding Hood” is with this function.
  • the corresponding keywords of the story classification. In other words, the keywords are related to the intelligent hardware business. So continue with the following steps:
  • Step S130 matching the extracted keywords with the vocabulary related to the intelligent hardware service, and selecting one or more candidate words matching the keywords from the vocabulary.
  • the user's pronunciation may not be standard, which may cause the sound to be unsatisfactory.
  • the speech recognition technology converts the voice signal.
  • the text message may be "Shaw Red Hat.”
  • the word does not match the "Little Red Riding Hood” completely, but the similarity between the two is very high. It can be concluded from the manual judgment that the child actually wants to express "Little Red Riding Hood”. But in fact, the text message into which the speech signal is converted can blur the matching of the keywords. For example, there may be a story of "Little Red Cat" in the story library. Then for a keyword, the candidate words selected from the vocabulary may be one or more.
  • Step S140 Perform error correction processing on the keywords in the text information according to the selected one or more candidate words.
  • the method shown in FIG. 1 firstly uses the speech recognition technology to perform speech recognition on the speech signal received by the intelligent hardware device, converts it into text information, and further analyzes the text information to obtain a plurality of keywords, and these keys are The words are matched by a vocabulary related to the intelligent hardware service, one or more candidate words are determined, and finally the keywords are corrected using the obtained candidate words.
  • the technical solution fully considers the functional characteristics of the intelligent hardware, and uses the preset business-related vocabulary to intelligently correct the keywords parsed in the speech recognition result, which significantly improves the accuracy of the speech recognition and consumes less resources. In line with the low energy consumption requirements of intelligent hardware devices.
  • the method shown in FIG. 1 further includes: presetting one or more fixed sentence patterns associated with the business voice interaction of the smart hardware device; marking in each fixed sentence pattern Position of the keyword; extracting the keyword from the text information includes: matching the text information with one or more fixed sentence patterns; extracting from the corresponding position of the text information according to the position of the keyword marked in the matched fixed sentence pattern Key words.
  • "I want to listen to the story - Xiao Hong Cap” is a fixed sentence style, which can be summarized as “I want to hear the story XXX", wherein "XXX” corresponds to a keyword.
  • the semantics of the sentence can also be expressed as: “I want to hear the story of Xiao Hong Cap”, then the fixed sentence is "I want to hear the story of XXX.”
  • the user can use the phrase "I want to listen to XXX's XXX", for example, "I want to listen to Andy Lau's forgotten water", and so on.
  • the method further includes: marking type information for each keyword in the fixed sentence; determining type information of the vocabulary related to the intelligent hardware service; and extracting the extracted keyword and the intelligent hardware
  • the matching of the vocabulary related to the service includes: determining the type information of the extracted keyword according to the type information of the keyword in the matched fixed sentence pattern, and extracting the extracted keyword and type according to the type information of the extracted keyword. Match the vocabulary to match.
  • the vocabulary related to the business can be the name of the story; for the business function of “song playing”, the vocabulary related to the business can be the name of the song, the style of the song, the singer Name and so on.
  • the position of each keyword in the fixed sentence pattern can be determined. Then, by using the type information corresponding to the keyword of each position, it can be determined which service-related vocabulary should be used to match the candidate words. .
  • the true semantics of the keywords can be determined according to the candidate words. For example, the story that a child wants to hear is the story of "Little Red Cat” or the story of "Little Red Riding Hood". In an embodiment of the present invention, in the method shown in FIG.
  • the error correction processing on the keywords in the text information according to the selected one or more candidate words includes: matching the selected keywords with the keywords Each candidate word is scored according to the matching degree of the extracted keyword and the candidate word; if the score of the highest score candidate word of the keyword is higher than or equal to the first confidence value, the highest score candidate is used The word corrects the keyword; if the score of the highest score candidate of the keyword is higher than the second confidence value but lower than the first confidence value, a further voice dialogue is performed with the user to confirm whether the highest score candidate is needed The keyword is corrected; if the score of the highest score candidate of the keyword is lower than or equal to the second confidence value, no correction is made.
  • the candidates for "Red Riding Hood” have the words “Little Red Cat” and “Little Red Riding Hood”.
  • “Red Riding Hood” and “Little Red Riding Hood” have the same two words, only one word is different; “Shaw Red Hat” has the same word as “Little Red Cat”, and the other two words have different voices.
  • the score result may be: “Little Red Riding Hood” has a score of 0.6, and “Little Red Cat” has a score of 0.5.
  • taking the first confidence value of 0.45 as an example, since the scores of the two candidate words are all higher than 0.45, then the candidate word with the highest score, that is, "Little Red Riding Hood” is selected to correct the keyword "Red Hood”.
  • the scoring of the candidate words according to the matching degree between the extracted keywords and the candidate words includes: From high to low, it is divided into three ranges of high, medium and low; if the keyword is the same as the pinyin of the candidate, but the pitch is different, it is scored in the high range; if the keyword is in the pinyin of the candidate If the initial or final part is the same, the score is scored in the middle range; if the key is not the same as the initial and final in the pinyin of the candidate, the score is scored in the low range.
  • the high-end score range is [0.45, 1]
  • the mid-range score is (0.4, 0.45)
  • the low-range score is [0, 0.4). Since “Little Red Cat” and “Little Red Riding Hood” and “Red Hood” belong to “the same pinyin, but the tone is different”, then the high-grade scoring standard is adopted. Let's look at another example: the pronunciation of the initials of some users "n” and “l” is not divided, resulting in the "beef” being said to be “flowing meat”, then the keyword “beef” and the word “flowing meat” The partial initials are different, the finals are the same, then the mid-range scoring standard is adopted.
  • the confidence of the candidate word of the highest score can be determined according to the method in the previous embodiment. For example, if the score for "beef" is 0.44, which is lower than the first confidence value of 0.45 but higher than the second confidence value of 0.4, then you can ask the user: "Do you want to say 'beef'?" to confirm if you need to use "Beef" corrects "flowing meat.” If the score of the highest score candidate of the keyword is lower than or equal to the second confidence value, no correction is made, because even if correction is made at this time, the user's original intention may be deviated.
  • the method further includes: if a plurality of keywords are extracted from the text information, multiplying the scores of the highest score candidates of each keyword to obtain a score of the plurality of keywords; If the scores of the plurality of keywords are higher than or equal to the third confidence value, correcting each keyword with the highest score candidate of each keyword; if the score of the plurality of keywords is higher than the fourth confidence value but lower than the first The three-confidence value is further voiced with the user to confirm whether the highest score candidate of each keyword is needed to correct each keyword; if the score of the plurality of keywords is lower than or equal to the fourth confidence value, no correct.
  • an example of how to calculate a candidate word score when a keyword is plural is given.
  • the score of "Zhang Dehua” is 0.3
  • the pronunciation of the user who "forgets the water” is very standard
  • the method further includes: outputting, according to the result of the correction processing, a corresponding service service of the smart hardware device.
  • the child without performing the technical solution of the present invention, the child said to the intelligent accompanying robot: "Do you tell the story of Xiao Hong Cap?", because "Shaw Red Hat” does not match “Little Red Riding Hood", the child gets The reply is "I will not do this.”
  • the mother re-directed the smart accompanying robot to "tell the story of Little Red Riding Hood", and the intelligent accompanying robot correctly obtained the resources of the story “Little Red Riding Hood” for voicetelling. .
  • FIG. 2 is a schematic structural diagram of a voice recognition error correction apparatus in an intelligent hardware device according to an embodiment of the present invention.
  • the voice recognition error correction apparatus 200 in the smart hardware device includes:
  • the voice recognition unit 210 is adapted to convert the voice signal received by the smart hardware device into text information by using a voice recognition technology.
  • the keyword extracting unit 220 is adapted to extract keywords from the text information.
  • the matching unit 230 is adapted to match the extracted keywords with the vocabulary related to the intelligent hardware service, and select one or more candidate words that match the keywords from the vocabulary.
  • the error correction unit 240 is adapted to perform error correction processing on the keywords in the text information according to the selected one or more candidate words.
  • the device shown in FIG. 2 firstly uses the voice recognition technology to perform voice recognition on the voice signal received by the intelligent hardware device by using the mutual recognition of each unit, converts it into text information, and further analyzes the text information to obtain some of them.
  • the keywords are matched by the vocabulary related to the intelligent hardware service to determine one or more candidate words, and finally the keywords are corrected by using the obtained candidate words.
  • the technical solution fully considers the functional characteristics of the intelligent hardware, and uses the preset business-related vocabulary to intelligently correct the keywords parsed in the speech recognition result, which significantly improves the accuracy of the speech recognition and consumes less resources. In line with the low energy consumption requirements of intelligent hardware devices.
  • the apparatus further includes: a configuration unit, configured to preset one or more fixed sentence patterns associated with the business voice interaction of the smart hardware device; in each fixed sentence pattern Marking the location of the keyword; the keyword extracting unit 220 is adapted to match the text information with one or more fixed sentence patterns; and extracting from the corresponding position of the text information according to the position of the keyword marked in the matched fixed sentence pattern Key words.
  • a configuration unit configured to preset one or more fixed sentence patterns associated with the business voice interaction of the smart hardware device; in each fixed sentence pattern Marking the location of the keyword; the keyword extracting unit 220 is adapted to match the text information with one or more fixed sentence patterns; and extracting from the corresponding position of the text information according to the position of the keyword marked in the matched fixed sentence pattern Key words.
  • the configuration unit is further adapted to mark type information for the keywords in each fixed sentence; determine type information of the vocabulary related to the intelligent hardware service; and the matching unit 230
  • the type information of the extracted keyword is determined according to the type information of the keyword in the matched fixed sentence pattern, and the extracted keyword is matched with the vocabulary matching the type according to the type information of the extracted keyword.
  • the error correcting unit 240 is adapted to select, for each selected candidate word that matches the keyword, according to the matching degree of the extracted keyword and the candidate word, the candidate Word scoring; if the score of the highest score candidate of the keyword is higher than or equal to the first confidence value, correct the keyword with the highest score candidate; if the score of the highest score candidate of the keyword is higher than the second confidence If the value is lower than the first confidence value, a further voice dialogue is performed with the user to confirm whether the keyword needs to be corrected with the highest score candidate; if the score of the highest score candidate of the keyword is lower than or equal to the second confidence Degree value, no correction.
  • the error correction unit 240 is adapted to divide the score from high to low into three ranges of high, medium, and low;
  • the score is scored in the high-end range
  • the keyword is the same as the initial or final part of the pinyin of the candidate word, it is scored in the mid-range range; if the keyword is not the same as the initial or final in the pinyin of the candidate word, it is in the low range hit Minute.
  • the error correction unit 240 is further adapted to multiply the scores of the highest score candidate words of each keyword when the plurality of keywords are extracted from the text information, to obtain the a score of a plurality of keywords; if the scores of the plurality of keywords are higher than or equal to the third confidence value, correcting each keyword with the highest score candidate of each keyword; if the score of the plurality of keywords is higher than the fourth If the confidence value is lower than the third confidence value, a further voice dialogue is performed with the user to confirm whether the highest score candidate of each keyword is needed to correct each keyword; if the scores of the multiple keywords are lower than or equal to the first Four confidence values are not corrected.
  • the apparatus further includes: a service service unit adapted to output a corresponding service service of the smart hardware device according to the result of the correction process.
  • the technical solution of the present invention firstly uses voice recognition technology to perform voice recognition on a voice signal received by an intelligent hardware device, converts it into text information, and further analyzes the text information to obtain a plurality of keywords thereof. These keywords are matched by a vocabulary related to the intelligent hardware service to determine one or more candidate words, and finally the keywords are corrected using the obtained candidate words.
  • the technical solution fully considers the functional characteristics of the intelligent hardware, and uses the preset business-related vocabulary to intelligently correct the keywords parsed in the speech recognition result, which significantly improves the accuracy of the speech recognition and consumes less resources. In line with the low energy consumption requirements of intelligent hardware devices.
  • modules in the devices of the embodiments can be adaptively changed and placed in one or more devices different from the embodiment.
  • the modules or units or components of the embodiments may be combined into one module or unit or component, and further they may be divided into a plurality of sub-modules or sub-units or sub-components.
  • any combination of the features disclosed in the specification, including the accompanying claims, the abstract and the drawings, and any methods so disclosed, or All processes or units of the device are combined.
  • Each feature disclosed in this specification (including the accompanying claims, the abstract and the drawings) may be replaced by alternative features that provide the same, equivalent or similar purpose.
  • the various component embodiments of the present invention may be implemented in hardware, or in a software module running on one or more processors, or in a combination thereof.
  • a microprocessor or digital signal processor may be used in practice to implement some of some or all of the components of the speech recognition error correction device in an intelligent hardware device in accordance with an embodiment of the present invention. Or all features.
  • the invention can also be implemented as a device or device program (e.g., a computer program and a computer program product) for performing some or all of the methods described herein.
  • a program implementing the invention may be stored on a computer readable medium or may be in the form of one or more signals. Such signals may be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.
  • Figure 3 shows a block diagram of a computing device for performing the method in accordance with the present invention.
  • the computing device conventionally includes a processor 310 and a computer program product or computer readable medium in the form of a memory 320.
  • the memory 320 may be an electronic memory such as a flash memory, an EEPROM (Electrically Erasable Programmable Read Only Memory), an EPROM, a hard disk, or a ROM.
  • the memory 320 has a storage space 330 that stores program code 331 for performing any of the method steps described above.
  • the storage space 330 for storing program code may separately store respective program codes 331 for implementing various steps in the above method.
  • the program code can be read from or written to one or more computer program products to the one or more computer programs In the product.
  • These computer program products include program code carriers such as hard disks, compact disks (CDs), memory cards or floppy disks.
  • Such computer program products are typically portable or fixed storage units such as those shown in FIG.
  • the storage unit may have storage segments, storage spaces, and the like that are similarly arranged to memory 320 in the computing device of FIG.
  • the program code can be compressed in an appropriate form.
  • the storage unit stores computer readable program code 331' for performing the steps of the method according to the invention, ie program code readable by a processor such as 310, when the program code is run by the computing device, resulting in The computing device performs the various steps in the methods described above.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Computational Linguistics (AREA)
  • Health & Medical Sciences (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Human Computer Interaction (AREA)
  • Acoustics & Sound (AREA)
  • Multimedia (AREA)
  • Artificial Intelligence (AREA)
  • Theoretical Computer Science (AREA)
  • General Health & Medical Sciences (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Machine Translation (AREA)

Abstract

L'invention concerne un procédé et un appareil de connexion d'erreur de reconnaissance vocale dans un dispositif matériel intelligent. Le procédé consiste : à convertir un signal vocal reçu par un dispositif matériel intelligent en informations textuelles au moyen d'une technologie de reconnaissance vocale (S110) ; à extraire des mots clés des informations textuelles (S120) ; à mettre en correspondance les mots clés extraits avec une liste de mots associée à un service matériel intelligent, et à sélectionner de la liste de mots un ou une pluralité de termes candidats correspondant aux mots clés (S130) ; et, sur la base du ou des termes candidats sélectionnés, à effectuer un traitement de correction d'erreur des mots clés dans les informations textuelles (S140). Le présent procédé prend en compte les performances fonctionnelles du matériel intelligent, et utilise une liste de mots associée au service prédéfinie pour effectuer une correction d'erreur intelligente de mots clés analysés à partir des résultats de reconnaissance vocale, ce qui augmente considérablement la précision de la reconnaissance vocale et occupe peu de ressources, et de ce fait répond aux exigences relatives à une faible consommation d'énergie de dispositif matériel intelligent.
PCT/CN2017/116165 2016-12-29 2017-12-14 Procédé et appareil de connexion d'erreur de reconnaissance vocale dans un dispositif matériel intelligent WO2018121275A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201611246867.0 2016-12-29
CN201611246867.0A CN106710592B (zh) 2016-12-29 2016-12-29 一种智能硬件设备中的语音识别纠错方法和装置

Publications (1)

Publication Number Publication Date
WO2018121275A1 true WO2018121275A1 (fr) 2018-07-05

Family

ID=58906036

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2017/116165 WO2018121275A1 (fr) 2016-12-29 2017-12-14 Procédé et appareil de connexion d'erreur de reconnaissance vocale dans un dispositif matériel intelligent

Country Status (2)

Country Link
CN (1) CN106710592B (fr)
WO (1) WO2018121275A1 (fr)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109977412A (zh) * 2019-03-29 2019-07-05 北京林业大学 一种字段值纠错方法、装置、可读介质及存储控制器
CN110232129A (zh) * 2019-06-11 2019-09-13 北京百度网讯科技有限公司 场景纠错方法、装置、设备和存储介质
CN110516237A (zh) * 2019-08-15 2019-11-29 重庆长安汽车股份有限公司 短文本短语提取方法、系统及存储介质
CN113808577A (zh) * 2021-09-18 2021-12-17 平安银行股份有限公司 语音摘要的智能提取方法、装置、电子设备及存储介质

Families Citing this family (22)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106710592B (zh) * 2016-12-29 2021-05-18 北京奇虎科技有限公司 一种智能硬件设备中的语音识别纠错方法和装置
CN107239547B (zh) * 2017-06-05 2019-05-28 北京儒博科技有限公司 用于语音点歌的语音纠错方法、终端及存储介质
TWI660340B (zh) * 2017-11-03 2019-05-21 財團法人資訊工業策進會 聲控方法及系統
CN108021554A (zh) * 2017-11-14 2018-05-11 无锡小天鹅股份有限公司 语音识别方法、装置以及洗衣机
CN107977356B (zh) * 2017-11-21 2019-10-25 新疆科大讯飞信息科技有限责任公司 识别文本纠错方法及装置
CN109947264B (zh) * 2017-12-21 2023-03-14 北京搜狗科技发展有限公司 一种信息展现方法、装置及电子设备
CN110120986B (zh) * 2018-02-05 2021-10-19 腾讯科技（深圳）有限公司 一种电子设备信息的获取方法、装置及设备
CN108647346B (zh) * 2018-05-15 2021-10-29 苏州东巍网络科技有限公司 一种用于可穿戴电子设备的老年人语音交互方法和系统
CN108877792B (zh) * 2018-05-30 2023-10-24 北京百度网讯科技有限公司 用于处理语音对话的方法、装置、电子设备以及计算机可读存储介质
CN109522550B (zh) * 2018-11-08 2023-04-07 和美（深圳）信息技术股份有限公司 文本信息纠错方法、装置、计算机设备和存储介质
CN109951354B (zh) * 2019-03-12 2021-08-10 北京奇虎科技有限公司 一种终端设备识别方法、系统及存储介质
CN110211592A (zh) * 2019-05-17 2019-09-06 北京华控创为南京信息技术有限公司 智能语音数据处理装置及方法
CN110232921A (zh) * 2019-06-21 2019-09-13 深圳市酷开网络科技有限公司 基于生活服务的语音操作方法、装置、智能电视及系统
CN112668312A (zh) * 2019-09-30 2021-04-16 北大方正集团有限公司 错别字修正方法、装置、电子设备及存储介质
CN110909127A (zh) * 2019-11-07 2020-03-24 中铁大桥科学研究院有限公司 一种用于桥梁巡检信息录入查询的方法及系统
CN110689891A (zh) * 2019-11-20 2020-01-14 广东奥园奥买家电子商务有限公司 一种基于公众显示装置的语音交互方法以及设备
CN111160013B (zh) * 2019-12-30 2023-11-24 北京百度网讯科技有限公司 文本纠错方法及装置
CN113763944B (zh) * 2020-09-29 2024-06-04 浙江思考者科技有限公司 基于拟真人逻辑知识库的ai视频云交互系统
CN114678027A (zh) * 2020-12-24 2022-06-28 深圳Tcl新技术有限公司 语音识别结果的纠错方法、装置、终端设备及存储介质
CN112580324B (zh) * 2020-12-24 2023-07-25 北京百度网讯科技有限公司 文本纠错方法、装置、电子设备以及存储介质
CN113420547A (zh) * 2021-08-25 2021-09-21 深圳市豪华科技有限公司 即时通信软件的错别字纠错方法及相关设备
CN115438995B (zh) * 2022-09-21 2023-10-10 青岛酷特智能股份有限公司 一种基于知识图谱的服装定制企业的业务处理方法及设备

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7058575B2 (en) * 2001-06-27 2006-06-06 Intel Corporation Integrating keyword spotting with graph decoder to improve the robustness of speech recognition
CN102682763A (zh) * 2011-03-10 2012-09-19 北京三星通信技术研究有限公司 修正语音输入文本中命名实体词汇的方法、装置及终端
CN103186523A (zh) * 2011-12-30 2013-07-03 富泰华工业（深圳）有限公司 电子设备及其自然语言分析方法
CN104269168A (zh) * 2014-09-24 2015-01-07 上海伯释信息科技有限公司 一种工作效率较高的语音识别的方法
CN106710592A (zh) * 2016-12-29 2017-05-24 北京奇虎科技有限公司 一种智能硬件设备中的语音识别纠错方法和装置

Family Cites Families (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050049868A1 (en) * 2003-08-25 2005-03-03 Bellsouth Intellectual Property Corporation Speech recognition error identification method and system
CN1979638A (zh) * 2005-12-02 2007-06-13 中国科学院自动化研究所 一种语音识别结果纠错方法
CN101154228A (zh) * 2006-09-27 2008-04-02 西门子公司 一种分段模式匹配方法及其装置
JP4845118B2 (ja) * 2006-11-20 2011-12-28 富士通株式会社 音声認識装置、音声認識方法、および、音声認識プログラム
CN101655837B (zh) * 2009-09-08 2010-10-13 北京邮电大学 一种对语音识别后文本进行检错并纠错的方法
CN103514882B (zh) * 2012-06-30 2017-11-10 北京百度网讯科技有限公司 一种语音识别方法及系统
JP2014137430A (ja) * 2013-01-16 2014-07-28 Sharp Corp 電子機器及び掃除機
CN105374356B (zh) * 2014-08-29 2019-07-30 株式会社理光 语音识别方法、语音评分方法、语音识别系统及语音评分系统
CN105632499B (zh) * 2014-10-31 2019-12-10 株式会社东芝 用于优化语音识别结果的方法和装置
CN105488027B (zh) * 2015-11-30 2019-07-12 百度在线网络技术（北京）有限公司 关键词的推送方法和装置
CN106098060B (zh) * 2016-05-19 2020-01-31 北京搜狗科技发展有限公司 语音的纠错处理方法和装置、用于语音的纠错处理的装置

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7058575B2 (en) * 2001-06-27 2006-06-06 Intel Corporation Integrating keyword spotting with graph decoder to improve the robustness of speech recognition
CN102682763A (zh) * 2011-03-10 2012-09-19 北京三星通信技术研究有限公司 修正语音输入文本中命名实体词汇的方法、装置及终端
CN103186523A (zh) * 2011-12-30 2013-07-03 富泰华工业（深圳）有限公司 电子设备及其自然语言分析方法
CN104269168A (zh) * 2014-09-24 2015-01-07 上海伯释信息科技有限公司 一种工作效率较高的语音识别的方法
CN106710592A (zh) * 2016-12-29 2017-05-24 北京奇虎科技有限公司 一种智能硬件设备中的语音识别纠错方法和装置

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109977412A (zh) * 2019-03-29 2019-07-05 北京林业大学 一种字段值纠错方法、装置、可读介质及存储控制器
CN109977412B (zh) * 2019-03-29 2022-12-27 北京林业大学 语音识别文本的字段值纠错方法、装置及存储控制器
CN110232129A (zh) * 2019-06-11 2019-09-13 北京百度网讯科技有限公司 场景纠错方法、装置、设备和存储介质
CN110232129B (zh) * 2019-06-11 2020-09-29 北京百度网讯科技有限公司 场景纠错方法、装置、设备和存储介质
CN110516237A (zh) * 2019-08-15 2019-11-29 重庆长安汽车股份有限公司 短文本短语提取方法、系统及存储介质
CN110516237B (zh) * 2019-08-15 2022-12-09 重庆长安汽车股份有限公司 短文本短语提取方法、系统及存储介质
CN113808577A (zh) * 2021-09-18 2021-12-17 平安银行股份有限公司 语音摘要的智能提取方法、装置、电子设备及存储介质

Also Published As

Publication number Publication date
CN106710592A (zh) 2017-05-24
CN106710592B (zh) 2021-05-18

Similar Documents

Publication Publication Date Title
WO2018121275A1 (fr) Procédé et appareil de connexion d'erreur de reconnaissance vocale dans un dispositif matériel intelligent
US11887590B2 (en) Voice enablement and disablement of speech processing functionality
US20200258506A1 (en) Domain and intent name feature identification and processing
US11564090B1 (en) Audio verification
US10339166B1 (en) Systems and methods for providing natural responses to commands
US8972260B2 (en) Speech recognition using multiple language models
CN108228132B (zh) 语音启用装置及其中执行的方法
CN107016994B (zh) 语音识别的方法及装置
US9990176B1 (en) Latency reduction for content playback
CN111710333B (zh) 用于生成语音转录的方法和系统
KR101670150B1 (ko) 이름 발음을 위한 시스템 및 방법
US9589562B2 (en) Pronunciation learning through correction logs
US9986394B1 (en) Voice-based messaging
TWI711967B (zh) 播報語音的確定方法、裝置和設備
US10628483B1 (en) Entity resolution with ranking
CN110494841B (zh) 语境语言翻译
CN104462071A (zh) 语音翻译设备和语音翻译方法
CN110750996B (zh) 多媒体信息的生成方法、装置及可读存储介质
WO2016183961A1 (fr) Procédé, système et dispositif de changement de l'interface d'un dispositif intelligent, et support d'informations non volatil pour ordinateur
CN106528715B (zh) 一种音频内容校核方法及装置
CN114758665B (zh) 音频数据增强方法、装置、电子设备及存储介质
US11563708B1 (en) Message grouping
CN110895938B (zh) 语音校正系统及语音校正方法
US11632345B1 (en) Message management for communal account
JP2007163967A (ja) 音声認識装置及び音声認識方法

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: 17886786

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 17886786

Country of ref document: EP

Kind code of ref document: A1