CN113704405A - Quality control scoring method, device, equipment and storage medium based on recording content - Google Patents
Quality control scoring method, device, equipment and storage medium based on recording content Download PDFInfo
- Publication number
- CN113704405A CN113704405A CN202111002989.6A CN202111002989A CN113704405A CN 113704405 A CN113704405 A CN 113704405A CN 202111002989 A CN202111002989 A CN 202111002989A CN 113704405 A CN113704405 A CN 113704405A
- Authority
- CN
- China
- Prior art keywords
- original
- data
- text data
- preset
- scoring
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000003908 quality control method Methods 0.000 title claims abstract description 41
- 238000013077 scoring method Methods 0.000 title claims abstract description 21
- 238000007689 inspection Methods 0.000 claims abstract description 39
- 230000006870 function Effects 0.000 claims description 75
- 239000013598 vector Substances 0.000 claims description 42
- 238000012545 processing Methods 0.000 claims description 25
- 230000011218 segmentation Effects 0.000 claims description 24
- 238000006243 chemical reaction Methods 0.000 claims description 21
- 238000000034 method Methods 0.000 claims description 18
- 238000004590 computer program Methods 0.000 claims description 11
- 239000011159 matrix material Substances 0.000 claims description 8
- 230000004913 activation Effects 0.000 claims description 6
- 238000013507 mapping Methods 0.000 claims description 4
- 238000005516 engineering process Methods 0.000 abstract description 8
- 238000013473 artificial intelligence Methods 0.000 abstract description 5
- 238000004891 communication Methods 0.000 description 10
- 239000003795 chemical substances by application Substances 0.000 description 6
- 238000007726 management method Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 230000008451 emotion Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000001228 spectrum Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 238000009432 framing Methods 0.000 description 2
- 239000004973 liquid crystal related substance Substances 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
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
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/3331—Query processing
- G06F16/334—Query execution
- G06F16/3343—Query execution using phonetics
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/35—Clustering; Classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/10—Text processing
- G06F40/12—Use of codes for handling textual entities
- G06F40/151—Transformation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/279—Recognition of textual entities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
-
- 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
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- 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
- G06Q30/01—Customer relationship services
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- General Engineering & Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Economics (AREA)
- Development Economics (AREA)
- Strategic Management (AREA)
- Artificial Intelligence (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Educational Administration (AREA)
- Marketing (AREA)
- Databases & Information Systems (AREA)
- Entrepreneurship & Innovation (AREA)
- General Business, Economics & Management (AREA)
- Biomedical Technology (AREA)
- Accounting & Taxation (AREA)
- Molecular Biology (AREA)
- Evolutionary Computation (AREA)
- Computing Systems (AREA)
- Biophysics (AREA)
- Software Systems (AREA)
- Finance (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Life Sciences & Earth Sciences (AREA)
- Mathematical Physics (AREA)
- Acoustics & Sound (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention relates to artificial intelligence and digital medical technology, and discloses a quality inspection scoring method based on recording content, which comprises the following steps: converting original recording data into original text data, comparing the original text data with a preset keyword blacklist, generating a keyword score according to a result obtained by comparison, constructing a hyperplane function according to the original text data, classifying the original text data by using the hyperplane function to obtain a classification result, scoring the classification result to obtain a classification score, and obtaining a quality inspection score according to the keyword score, the classification score and a preset quality inspection scoring formula. In the invention, the original recording data can be recording data between doctors and patients. In addition, the invention also relates to a block chain technology, and the original sound recording data can be stored in the nodes of the block chain. The invention also provides a quality control scoring device based on the recording content, electronic equipment and a storage medium. The invention can judge the service quality of the agent to the client.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a quality inspection scoring method and device based on recording content, electronic equipment and a computer readable storage medium.
Background
When the seat receives the telephone of the customer, the seat can decide whether the problem of the customer is solved or not according to the judgment of the seat and whether a work order needs to be reported to further solve the problem of the customer or not. Because the seat has some manual errors when making the judgment, the problems of many customers are not really solved. Therefore, the quality inspection of the recorded content of the client is required, and corresponding operation is further performed according to the grade obtained by the quality inspection. Therefore, a quality inspection scoring method is urgently needed to be provided for evaluating the service quality of the agent to the client.
Disclosure of Invention
The invention provides a quality control scoring method and device based on recording content and a computer readable storage medium, and mainly aims to judge the service quality of an agent to a client.
In order to achieve the above object, the quality control scoring method based on the recorded sound content provided by the invention comprises the following steps:
acquiring original recording data, and performing text conversion on the original recording data to obtain original text data;
comparing the original text data with a preset keyword blacklist, and generating a keyword score according to a result obtained by comparison;
constructing a hyperplane function according to the original text data, and classifying the original text data by using the hyperplane function to obtain a classification result;
grading the classification result to obtain a classification grade;
and taking the keyword scores and the classification scores as the input of a preset quality inspection scoring formula to obtain the quality inspection scores of the original recording data.
Optionally, the performing text conversion on the original sound recording data to obtain original text data includes:
identifying a mute section in the original recording data, and executing cutting-off processing on the mute section to obtain initial recording data;
extracting the characteristics of the initial recording data to obtain a characteristic vector set;
and performing voice recognition on the feature vector set according to a preset acoustic model, a language model and a dictionary to obtain original text data.
Optionally, the performing speech recognition on the feature vector set according to a preset acoustic model, a language model and a dictionary to obtain original text data includes:
performing phoneme processing on the feature vector set by using a preset acoustic model to obtain phoneme information;
obtaining a single word or a single word corresponding to the phoneme information based on a preset dictionary;
and identifying the association probability value between the single words or the single words by using a preset language model, and combining the single words or the single words into the original text data according to the probability value.
Optionally, the identifying, with the language model, association probability values between the single words or single words includes:
vectorizing the single character or the single word to obtain a character vector corresponding to the single character and a word vector corresponding to the single word;
converting the word vectors or the word vectors according to a forward long and short memory network layer and a backward long and short memory network layer in the language model to obtain a vector matrix;
and inputting the vector matrix into a preset activation function to obtain the association probability value of a single word or a single word.
Optionally, the comparing the original text data with a preset keyword blacklist, and generating a keyword score according to a result obtained by the comparing includes:
performing word segmentation processing on the original text data to obtain a word segmentation set;
comparing the participles in the participle set with the keywords in the keyword blacklist, and summarizing the number of the participles which are overlapped with the keyword blacklist to obtain an overlapped number;
and generating corresponding keyword scores according to different preset numerical value intervals to which the coincidence numbers belong.
Optionally, the constructing a hyperplane function according to the original text data includes:
counting the total number of data corresponding to the original text data, and taking the total number of data as a characteristic dimension;
acquiring a preset classification tag set, and analyzing the classification tag set to obtain the total number of tags;
constructing a multi-dimensional coordinate system by using the characteristic dimension and the total number of the labels;
mapping the original text data to the multi-dimensional coordinate system to obtain a text coordinate set;
calculating a distance value between any two text coordinates in the text coordinate set;
sorting the distance values, and selecting two text coordinates corresponding to the minimum distance value as a first text coordinate and a second text coordinate respectively;
constructing a hyperplane by taking the first text coordinate as a left boundary and the second text coordinate as a right boundary;
and selecting the center of the hyperplane to establish a hyperplane function.
Optionally, the classifying the original text data by using the hyperplane function to obtain a classification result includes:
respectively calculating distance values between the hyperplane function and the first text coordinate and the second text coordinate, and constructing a minimum distance function according to the distance values;
obtaining a preset constraint condition, and solving a minimum distance function based on the constraint condition by using a Lagrange function to obtain a hyperplane;
and classifying the original text data by utilizing the hyperplane to obtain a classification result.
In order to solve the above problem, the present invention further provides a quality control scoring apparatus based on recorded sound content, the apparatus comprising:
the system comprises a text conversion module, a voice recording module and a voice recording module, wherein the text conversion module is used for acquiring original voice recording data and performing text conversion on the original voice recording data to obtain original text data;
the keyword comparison module is used for comparing the original text data with a preset keyword blacklist and generating a keyword score according to a result obtained by comparison;
the text classification module is used for constructing a hyperplane function according to the original text data, classifying the original text data by using the hyperplane function to obtain a classification result, and grading the classification result to obtain a classification grade;
and the quality inspection scoring module is used for taking the keyword scoring and the classification scoring as the input of a preset quality inspection scoring formula to obtain the quality inspection scoring of the original recording data.
In order to solve the above problem, the present invention also provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the method for quality control scoring based on recorded sound content as described above.
In order to solve the above problem, the present invention further provides a computer-readable storage medium, in which at least one computer program is stored, and the at least one computer program is executed by a processor in an electronic device to implement the quality control scoring method based on recorded sound content.
The embodiment of the invention converts the recording data into the text data, expresses the text data in a text form and is convenient to analyze; further, comparing the text data with a preset keyword blacklist, and carrying out primary scoring on the text data from the perspective of the keyword blacklist; classifying the text data by constructing a hyperplane function, primarily grading the text data according to the classification result, and obtaining a final quality control grade according to the two grades. The embodiment of the invention considers the grading of more than one dimension and can judge the service quality of the customer by the agent more accurately. Therefore, the quality control scoring method, the quality control scoring device, the electronic equipment and the computer readable storage medium based on the recording content can judge the service quality of the agent to the client.
Drawings
Fig. 1 is a schematic flowchart of a quality inspection scoring method based on recorded sound content according to an embodiment of the present invention;
FIG. 2 is a functional block diagram of an apparatus for quality control and scoring based on recorded sound according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device for implementing the quality inspection scoring method based on recorded sound content according to an embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the application provides a quality control scoring method based on recording content. The execution subject of the quality control scoring method based on the recorded sound content includes, but is not limited to, at least one of the electronic devices such as a server and a terminal, which can be configured to execute the method provided by the embodiment of the present application. In other words, the quality control scoring method based on the recorded content may be performed by software or hardware installed in the terminal device or the server device, and the software may be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like.
Fig. 1 is a schematic flow chart of a quality control scoring method based on recorded sound content according to an embodiment of the present invention. In this embodiment, the quality control scoring method based on the recorded sound content includes:
and S1, acquiring original recording data, and performing text conversion on the original recording data to obtain original text data.
In the embodiment of the invention, the original recording data is the telephone communication content of the seat and the client without reporting the work order in the intelligent customer service scene.
Specifically, the performing text conversion on the original sound recording data to obtain original text data includes:
identifying a mute section in the original recording data, and executing cutting-off processing on the mute section to obtain initial recording data;
extracting the characteristics of the initial recording data to obtain a characteristic vector set;
and performing voice recognition on the feature vector set according to a preset acoustic model, a language model and a dictionary to obtain original text data.
In detail, the silent section in the original recording data refers to data without sound in the audio for a long time, and performing the cutting-off processing on the silent section can save bandwidth resources occupied by the original recording data.
Further, feature extraction is performed on the initial recording data, that is, a series of processing such as pre-emphasis processing, framing processing, windowing processing, fast fourier transform and the like is performed on the initial recording data to obtain a frequency spectrum corresponding to the initial recording data, and discrete cosine change is performed on the frequency spectrum to obtain a feature vector set.
Specifically, the performing speech recognition on the feature vector set according to a preset acoustic model, a language model and a dictionary to obtain original text data includes:
performing phoneme processing on the feature vector set by using a preset acoustic model to obtain phoneme information;
obtaining a single word or a single word corresponding to the phoneme information based on a preset dictionary;
and identifying the association probability value between the single words or the single words by using a preset language model, and combining the single words or the single words into the original text data according to the probability value.
Preferably, the acoustic model may be a Bert model, and the language model may be a two-way long-short term memory network model.
In the embodiment of the present invention, the phoneme information includes phonetic symbols in english, and initials and finals in chinese, and the like. The dictionary comprises phoneme information and single words or words corresponding to the phonemes. According to the embodiment of the invention, the traversal operation is executed in the preset dictionary according to the phoneme information, so that a single word or word corresponding to the phoneme information is obtained.
Further, the identifying, with the language model, association probability values between the single words or single words includes:
vectorizing the single character or the single word to obtain a character vector corresponding to the single character and a word vector corresponding to the single word;
converting the word vectors or the word vectors according to a forward long and short memory network layer and a backward long and short memory network layer in the language model to obtain a vector matrix;
and inputting the vector matrix into a preset activation function to obtain the association probability value of a single word or a single word.
Preferably, the activation function may be a softmax function.
Further, in the embodiment of the present invention, the single word or the single word is recognized as an entire text set according to the probability value, the judgment is performed according to the probability value of the single word or the single word corresponding to the phoneme information and a preset probability threshold, the corresponding single word or the single word larger than the probability threshold is reserved, the corresponding single word or the single word smaller than or equal to the probability threshold is deleted, and the reserved single word or the reserved single word is recognized as the text set.
For example, the probability value that the language model identifies the individual words or phrases as being related to each other is: i: 0.0786, is: 0.0546, i are: 0.0967, customer service: 0.06785, customer service personnel: 0.0898, the probability threshold is 0.08, so that the 'I is' and the 'customer service staff' are kept, the rest is deleted, and the recognized text is 'I is the customer service staff'.
In another embodiment of the present invention, an ASR speech recognition technology may be used to perform text conversion on the original recording data to obtain original text data. Among other things, the ASR Speech Recognition technology, also known as Automatic Speech Recognition (ASR), aims at converting the vocabulary content in human Speech into computer-readable input, such as keystrokes, binary codes or character sequences. Unlike speaker recognition and speaker verification, the latter attempts to recognize or verify the speaker who uttered the speech rather than the vocabulary content contained therein.
S2, comparing the original text data with a preset keyword blacklist, and generating a keyword score according to a result obtained by comparison.
In the embodiment of the invention, the preset keyword blacklist comprises words which are not allowed to be proposed or strictly prohibited in a customer service scene, and when the original text data contains the keywords which are coincident with the keywords in the keyword blacklist, the subsequent keyword scoring can be influenced.
Specifically, the comparing the original text data with a preset keyword blacklist, and generating a keyword score according to a result obtained by the comparing includes:
performing word segmentation processing on the original text data to obtain a word segmentation set;
comparing the participles in the participle set with the keywords in the keyword blacklist, and summarizing the number of the participles which are overlapped with the keyword blacklist to obtain an overlapped number;
and generating corresponding keyword scores according to different preset numerical value intervals to which the coincidence numbers belong.
In detail, the word segmentation process may use a reference word segmentation device to perform word segmentation, wherein the reference word segmentation device may be a word segmentation device of hafford, a word embedding + Bi-LSTM + CRF word segmentation device, a ZPar word segmentation device, or a word segmentation tool of jiba.
For example, the preset first numerical range is 0 to 10, the preset second numerical range is 11 to 20, the preset third numerical range is not 21 to 30, if the coincidence number belongs to the first numerical range, the corresponding keyword score is 30, and so on.
S3, constructing a hyperplane function according to the original text data, and classifying the original text data by using the hyperplane function to obtain a classification result.
In an embodiment of the present invention, the constructing a hyperplane function according to the original text data includes:
counting the total number of data corresponding to the original text data, and taking the total number of data as a characteristic dimension;
acquiring a preset classification tag set, and analyzing the classification tag set to obtain the total number of tags;
constructing a multi-dimensional coordinate system by using the characteristic dimension and the total number of the labels;
mapping the original text data to the multi-dimensional coordinate system to obtain a text coordinate set;
calculating a distance value between any two text coordinates in the text coordinate set;
sorting the distance values, and selecting two text coordinates corresponding to the minimum distance value as a first text coordinate and a second text coordinate respectively;
constructing a hyperplane by taking the first text coordinate as a left boundary and the second text coordinate as a right boundary;
and selecting the center of the hyperplane to establish a hyperplane function.
And the preset classification label set is the emotion label of the client.
For example, the total data amount of the original text data is used as a feature dimension, if two feature subsets exist, the feature dimension is 2, the tag set is used as a y-axis, the feature subsets construct a two-dimensional coordinate system for an x-axis, and the feature subsets are mapped onto the two-dimensional coordinate system to obtain a feature coordinate set on the two-dimensional coordinate system. And taking the first text coordinate as a left boundary and the second text coordinate as a right boundary, wherein the function of the left boundary can be w x + b-1, and the function of the right boundary can be w x + b-1, so that the hyperplane function is w x + b-0.
Specifically, the classifying the original text data by using the hyperplane function to obtain a classification result includes:
respectively calculating distance values between the hyperplane function and the first text coordinate and the second text coordinate, and constructing a minimum distance function according to the distance values;
obtaining a preset constraint condition, and solving a minimum distance function based on the constraint condition by using a Lagrange function to obtain a hyperplane;
and classifying the original text data by utilizing the hyperplane to obtain a classification result.
Further, said calculating distance values between said hyperplane function and said first text coordinate and said second text coordinate, respectively, comprises:
wherein, γiIs a distance value, xiIs the ith text coordinate, yiFor said classification label setThe ith class label, w and b are preset fixed parameters.
In detail, in the embodiment of the present invention, the preset constraint condition is that the distance from each coordinate to the hyperplane is greater than or equal to the minimum distance function.
And S4, scoring the classification result to obtain a classification score.
In the embodiment of the present invention, the scoring the classification result to obtain a classification score includes:
acquiring a preset classification rule table, wherein the classification rule table comprises classification categories and corresponding scores;
scoring the classification result according to the classification rule table to obtain a classification score,
in detail, scoring the classification result can increase the dimensionality of subsequent quality inspection scoring, so that the quality inspection scoring is more accurate.
And S5, taking the keyword scores and the classification scores as the input of a preset quality inspection scoring formula to obtain the quality inspection scores of the original sound recording data.
In the embodiment of the present invention, the using the keyword score and the classification score as the input of a preset quality inspection score formula includes:
the preset quality inspection scoring formula is as follows:
Score＝α*q1+β*q2
wherein Score is the quality control Score, q1 is the keyword Score, q2 is the classification Score, and α and β are preset weights.
In detail, the data which are most likely to be problematic can be sorted according to the quality control scores and then are given to service personnel for checking and checking one by one according to the recording contents and the converted texts. Meanwhile, the scheme relates to keyword blacklist and analysis on the emotion categories of the clients, so that the scheme is more accurate.
The embodiment of the invention converts the recording data into the text data, expresses the text data in a text form and is convenient to analyze; further, comparing the text data with a preset keyword blacklist, and carrying out primary scoring on the text data from the perspective of the keyword blacklist; classifying the text data by constructing a hyperplane function, primarily grading the text data according to the classification result, and obtaining a final quality control grade according to the two grades. The embodiment of the invention considers the grading of more than one dimension and can judge the service quality of the customer by the agent more accurately. Therefore, the quality inspection scoring method based on the recording content can realize the quality of service of the judge seat to the client.
Fig. 2 is a functional block diagram of a quality inspection and scoring apparatus based on recorded sound content according to an embodiment of the present invention.
The quality control scoring device 100 based on the recorded sound content can be installed in an electronic device. According to the implemented functions, the quality control scoring apparatus 100 based on the recorded sound content may include a text conversion module 101, a keyword comparison module 102, a text classification module 103, and a quality control scoring module 104. The module of the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the text conversion module 101 is configured to obtain original recording data, perform text conversion on the original recording data, and obtain original text data;
the keyword comparison module 102 is configured to compare the original text data with a preset keyword blacklist, and generate a keyword score according to a result obtained by the comparison;
the text classification module 103 is configured to construct a hyperplane function according to the original text data, classify the original text data by using the hyperplane function to obtain a classification result, and score the classification result to obtain a classification score;
the quality control scoring module 104 is configured to use the keyword score and the classification score as inputs of a preset quality control scoring formula to obtain a quality control score of the original sound recording data.
In detail, the quality control scoring apparatus 100 based on recorded sound content has the following specific implementation modes:
the method comprises the steps of firstly, obtaining original recording data, and performing text conversion on the original recording data to obtain original text data.
In the embodiment of the invention, the original recording data is the telephone communication content of the seat and the client without reporting the work order in the intelligent customer service scene.
Specifically, the performing text conversion on the original sound recording data to obtain original text data includes:
identifying a mute section in the original recording data, and executing cutting-off processing on the mute section to obtain initial recording data;
extracting the characteristics of the initial recording data to obtain a characteristic vector set;
and performing voice recognition on the feature vector set according to a preset acoustic model, a language model and a dictionary to obtain original text data.
In detail, the silent section in the original recording data refers to data without sound in the audio for a long time, and performing the cutting-off processing on the silent section can save bandwidth resources occupied by the original recording data.
Further, feature extraction is performed on the initial recording data, that is, a series of processing such as pre-emphasis processing, framing processing, windowing processing, fast fourier transform and the like is performed on the initial recording data to obtain a frequency spectrum corresponding to the initial recording data, and discrete cosine change is performed on the frequency spectrum to obtain a feature vector set.
Specifically, the performing speech recognition on the feature vector set according to a preset acoustic model, a language model and a dictionary to obtain original text data includes:
performing phoneme processing on the feature vector set by using a preset acoustic model to obtain phoneme information;
obtaining a single word or a single word corresponding to the phoneme information based on a preset dictionary;
and identifying the association probability value between the single words or the single words by using a preset language model, and combining the single words or the single words into the original text data according to the probability value.
Preferably, the acoustic model may be a Bert model, and the language model may be a two-way long-short term memory network model.
In the embodiment of the present invention, the phoneme information includes phonetic symbols in english, and initials and finals in chinese, and the like. The dictionary comprises phoneme information and single words or words corresponding to the phonemes. According to the embodiment of the invention, the traversal operation is executed in the preset dictionary according to the phoneme information, so that a single word or word corresponding to the phoneme information is obtained.
Further, the identifying, with the language model, association probability values between the single words or single words includes:
vectorizing the single character or the single word to obtain a character vector corresponding to the single character and a word vector corresponding to the single word;
converting the word vectors or the word vectors according to a forward long and short memory network layer and a backward long and short memory network layer in the language model to obtain a vector matrix;
and inputting the vector matrix into a preset activation function to obtain the association probability value of a single word or a single word.
Preferably, the activation function may be a softmax function.
Further, in the embodiment of the present invention, the single word or the single word is recognized as an entire text set according to the probability value, the judgment is performed according to the probability value of the single word or the single word corresponding to the phoneme information and a preset probability threshold, the corresponding single word or the single word larger than the probability threshold is reserved, the corresponding single word or the single word smaller than or equal to the probability threshold is deleted, and the reserved single word or the reserved single word is recognized as the text set.
For example, the probability value that the language model identifies the individual words or phrases as being related to each other is: i: 0.0786, is: 0.0546, i are: 0.0967, customer service: 0.06785, customer service personnel: 0.0898, the probability threshold is 0.08, so that the 'I is' and the 'customer service staff' are kept, the rest is deleted, and the recognized text is 'I is the customer service staff'.
In another embodiment of the present invention, an ASR speech recognition technology may be used to perform text conversion on the original recording data to obtain original text data. Among other things, the ASR Speech Recognition technology, also known as Automatic Speech Recognition (ASR), aims at converting the vocabulary content in human Speech into computer-readable input, such as keystrokes, binary codes or character sequences. Unlike speaker recognition and speaker verification, the latter attempts to recognize or verify the speaker who uttered the speech rather than the vocabulary content contained therein.
And step two, comparing the original text data with a preset keyword blacklist, and generating a keyword score according to a result obtained by comparison.
In the embodiment of the invention, the preset keyword blacklist comprises words which are not allowed to be proposed or strictly prohibited in a customer service scene, and when the original text data contains the keywords which are coincident with the keywords in the keyword blacklist, the subsequent keyword scoring can be influenced.
Specifically, the comparing the original text data with a preset keyword blacklist, and generating a keyword score according to a result obtained by the comparing includes:
performing word segmentation processing on the original text data to obtain a word segmentation set;
comparing the participles in the participle set with the keywords in the keyword blacklist, and summarizing the number of the participles which are overlapped with the keyword blacklist to obtain an overlapped number;
and generating corresponding keyword scores according to different preset numerical value intervals to which the coincidence numbers belong.
In detail, the word segmentation process may use a reference word segmentation device to perform word segmentation, wherein the reference word segmentation device may be a word segmentation device of hafford, a word embedding + Bi-LSTM + CRF word segmentation device, a ZPar word segmentation device, or a word segmentation tool of jiba.
For example, the preset first numerical range is 0 to 10, the preset second numerical range is 11 to 20, the preset third numerical range is not 21 to 30, if the coincidence number belongs to the first numerical range, the corresponding keyword score is 30, and so on.
And thirdly, constructing a hyperplane function according to the original text data, and classifying the original text data by using the hyperplane function to obtain a classification result.
In an embodiment of the present invention, the constructing a hyperplane function according to the original text data includes:
counting the total number of data corresponding to the original text data, and taking the total number of data as a characteristic dimension;
acquiring a preset classification tag set, and analyzing the classification tag set to obtain the total number of tags;
constructing a multi-dimensional coordinate system by using the characteristic dimension and the total number of the labels;
mapping the original text data to the multi-dimensional coordinate system to obtain a text coordinate set;
calculating a distance value between any two text coordinates in the text coordinate set;
sorting the distance values, and selecting two text coordinates corresponding to the minimum distance value as a first text coordinate and a second text coordinate respectively;
constructing a hyperplane by taking the first text coordinate as a left boundary and the second text coordinate as a right boundary;
and selecting the center of the hyperplane to establish a hyperplane function.
And the preset classification label set is the emotion label of the client.
For example, the total data amount of the original text data is used as a feature dimension, if two feature subsets exist, the feature dimension is 2, the tag set is used as a y-axis, the feature subsets construct a two-dimensional coordinate system for an x-axis, and the feature subsets are mapped onto the two-dimensional coordinate system to obtain a feature coordinate set on the two-dimensional coordinate system. And taking the first text coordinate as a left boundary and the second text coordinate as a right boundary, wherein the function of the left boundary can be w x + b-1, and the function of the right boundary can be w x + b-1, so that the hyperplane function is w x + b-0.
Specifically, the classifying the original text data by using the hyperplane function to obtain a classification result includes:
respectively calculating distance values between the hyperplane function and the first text coordinate and the second text coordinate, and constructing a minimum distance function according to the distance values;
obtaining a preset constraint condition, and solving a minimum distance function based on the constraint condition by using a Lagrange function to obtain a hyperplane;
and classifying the original text data by utilizing the hyperplane to obtain a classification result.
Further, said calculating distance values between said hyperplane function and said first text coordinate and said second text coordinate, respectively, comprises:
wherein, γiIs a distance value, xiIs the ith text coordinate, yiAnd w and b are preset fixed parameters for the ith classification label in the classification label set.
In detail, in the embodiment of the present invention, the preset constraint condition is that the distance from each coordinate to the hyperplane is greater than or equal to the minimum distance function.
And fourthly, scoring the classification result to obtain a classification score.
In the embodiment of the present invention, the scoring the classification result to obtain a classification score includes:
acquiring a preset classification rule table, wherein the classification rule table comprises classification categories and corresponding scores;
scoring the classification result according to the classification rule table to obtain a classification score,
in detail, scoring the classification result can increase the dimensionality of subsequent quality inspection scoring, so that the quality inspection scoring is more accurate.
And fifthly, taking the keyword scores and the classification scores as the input of a preset quality inspection scoring formula to obtain the quality inspection scores of the original recording data.
In the embodiment of the present invention, the using the keyword score and the classification score as the input of a preset quality inspection score formula includes:
the preset quality inspection scoring formula is as follows:
Score＝α*q1+β*q2
wherein Score is the quality control Score, q1 is the keyword Score, q2 is the classification Score, and α and β are preset weights.
In detail, the data which are most likely to be problematic can be sorted according to the quality control scores and then are given to service personnel for checking and checking one by one according to the recording contents and the converted texts. Meanwhile, the scheme relates to keyword blacklist and analysis on the emotion categories of the clients, so that the scheme is more accurate.
The embodiment of the invention converts the recording data into the text data, expresses the text data in a text form and is convenient to analyze; further, comparing the text data with a preset keyword blacklist, and carrying out primary scoring on the text data from the perspective of the keyword blacklist; classifying the text data by constructing a hyperplane function, primarily grading the text data according to the classification result, and obtaining a final quality control grade according to the two grades. The embodiment of the invention considers the grading of more than one dimension and can judge the service quality of the customer by the agent more accurately. Therefore, the quality inspection scoring device based on the recording content can realize the quality of service of the judge seat to the client.
Fig. 3 is a schematic structural diagram of an electronic device for implementing a quality inspection scoring method based on recorded sound content according to an embodiment of the present invention.
The electronic device 1 may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13, and may further include a computer program, such as a quality control scoring program based on recorded content, stored in the memory 11 and executable on the processor 10.
In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same function or different functions, and includes one or more Central Processing Units (CPUs), a microprocessor, a digital Processing chip, a graphics processor, a combination of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing programs or modules stored in the memory 11 (for example, executing a quality Control scoring program based on recorded contents, etc.), and calling data stored in the memory 11.
The memory 11 includes at least one type of readable storage medium including flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, for example a removable hard disk of the electronic device. The memory 11 may also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used to store not only application software installed in the electronic device and various types of data, such as codes of a quality control scoring program based on recorded contents, but also data that has been output or is to be output temporarily.
The communication bus 12 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
The communication interface 13 is used for communication between the electronic device and other devices, and includes a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which are typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.
Fig. 3 shows only an electronic device with components, and it will be understood by those skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than those shown, or some components may be combined, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management and the like are realized through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The quality control scoring program based on recorded content stored in the memory 11 of the electronic device 1 is a combination of a plurality of instructions, and when running in the processor 10, can realize:
acquiring original recording data, and performing text conversion on the original recording data to obtain original text data;
comparing the original text data with a preset keyword blacklist, and generating a keyword score according to a result obtained by comparison;
constructing a hyperplane function according to the original text data, and classifying the original text data by using the hyperplane function to obtain a classification result;
grading the classification result to obtain a classification grade;
and taking the keyword scores and the classification scores as the input of a preset quality inspection scoring formula to obtain the quality inspection scores of the original recording data.
Specifically, the specific implementation method of the instruction by the processor 10 may refer to the description of the relevant steps in the embodiment corresponding to the drawings, which is not described herein again.
Further, the integrated modules/units of the electronic device 1, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. The computer readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
The present invention also provides a computer-readable storage medium, storing a computer program which, when executed by a processor of an electronic device, may implement:
acquiring original recording data, and performing text conversion on the original recording data to obtain original text data;
comparing the original text data with a preset keyword blacklist, and generating a keyword score according to a result obtained by comparison;
constructing a hyperplane function according to the original text data, and classifying the original text data by using the hyperplane function to obtain a classification result;
grading the classification result to obtain a classification grade;
and taking the keyword scores and the classification scores as the input of a preset quality inspection scoring formula to obtain the quality inspection scores of the original recording data.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A quality control scoring method based on recorded sound content is characterized by comprising the following steps:
acquiring original recording data, and performing text conversion on the original recording data to obtain original text data;
comparing the original text data with a preset keyword blacklist, and generating a keyword score according to a result obtained by comparison;
constructing a hyperplane function according to the original text data, and classifying the original text data by using the hyperplane function to obtain a classification result;
grading the classification result to obtain a classification grade;
and taking the keyword scores and the classification scores as the input of a preset quality inspection scoring formula to obtain the quality inspection scores of the original recording data.
2. The method for quality control scoring based on recorded sound content of claim 1, wherein the text conversion of the original recorded sound data to obtain original text data comprises:
identifying a mute section in the original recording data, and executing cutting-off processing on the mute section to obtain initial recording data;
extracting the characteristics of the initial recording data to obtain a characteristic vector set;
and performing voice recognition on the feature vector set according to a preset acoustic model, a language model and a dictionary to obtain original text data.
3. The quality control scoring method based on the recording content of claim 2, wherein the performing speech recognition on the feature vector set according to a preset acoustic model, a language model and a dictionary to obtain original text data comprises:
performing phoneme processing on the feature vector set by using a preset acoustic model to obtain phoneme information;
obtaining a single word or a single word corresponding to the phoneme information based on a preset dictionary;
and identifying the association probability value between the single words or the single words by using a preset language model, and combining the single words or the single words into the original text data according to the probability value.
4. The method for scoring quality control based on recorded sound content according to claim 3, wherein the identifying the association probability values between the single words or single words by using the language model comprises:
vectorizing the single character or the single word to obtain a character vector corresponding to the single character and a word vector corresponding to the single word;
converting the word vectors or the word vectors according to a forward long and short memory network layer and a backward long and short memory network layer in the language model to obtain a vector matrix;
and inputting the vector matrix into a preset activation function to obtain the association probability value of a single word or a single word.
5. The method as claimed in claim 1, wherein the comparing the original text data with a predetermined keyword blacklist and generating a keyword score according to the comparing result comprises:
performing word segmentation processing on the original text data to obtain a word segmentation set;
comparing the participles in the participle set with the keywords in the keyword blacklist, and summarizing the number of the participles which are overlapped with the keyword blacklist to obtain an overlapped number;
and generating corresponding keyword scores according to different preset numerical value intervals to which the coincidence numbers belong.
6. The method of claim 1, wherein the constructing a hyperplane function from the raw text data comprises:
counting the total number of data corresponding to the original text data, and taking the total number of data as a characteristic dimension;
acquiring a preset classification tag set, and analyzing the classification tag set to obtain the total number of tags;
constructing a multi-dimensional coordinate system by using the characteristic dimension and the total number of the labels;
mapping the original text data to the multi-dimensional coordinate system to obtain a text coordinate set;
calculating a distance value between any two text coordinates in the text coordinate set;
sorting the distance values, and selecting two text coordinates corresponding to the minimum distance value as a first text coordinate and a second text coordinate respectively;
constructing a hyperplane by taking the first text coordinate as a left boundary and the second text coordinate as a right boundary;
and selecting the center of the hyperplane to establish a hyperplane function.
7. The method as claimed in claim 6, wherein the classifying the original text data by the hyperplane function to obtain a classification result comprises:
respectively calculating distance values between the hyperplane function and the first text coordinate and the second text coordinate, and constructing a minimum distance function according to the distance values;
obtaining a preset constraint condition, and solving a minimum distance function based on the constraint condition by using a Lagrange function to obtain a hyperplane;
and classifying the original text data by utilizing the hyperplane to obtain a classification result.
8. A quality control scoring apparatus based on recorded sound content, the apparatus comprising:
the system comprises a text conversion module, a voice recording module and a voice recording module, wherein the text conversion module is used for acquiring original voice recording data and performing text conversion on the original voice recording data to obtain original text data;
the keyword comparison module is used for comparing the original text data with a preset keyword blacklist and generating a keyword score according to a result obtained by comparison;
the text classification module is used for constructing a hyperplane function according to the original text data, classifying the original text data by using the hyperplane function to obtain a classification result, and grading the classification result to obtain a classification grade;
and the quality inspection scoring module is used for taking the keyword scoring and the classification scoring as the input of a preset quality inspection scoring formula to obtain the quality inspection scoring of the original recording data.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of quality control scoring based on recorded sound content of any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the quality control scoring method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111002989.6A CN113704405B (en) | 2021-08-30 | 2021-08-30 | Quality inspection scoring method, device, equipment and storage medium based on recorded content |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111002989.6A CN113704405B (en) | 2021-08-30 | 2021-08-30 | Quality inspection scoring method, device, equipment and storage medium based on recorded content |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113704405A true CN113704405A (en) | 2021-11-26 |
| CN113704405B CN113704405B (en) | 2024-06-28 |
Family
ID=78656641
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111002989.6A Active CN113704405B (en) | 2021-08-30 | 2021-08-30 | Quality inspection scoring method, device, equipment and storage medium based on recorded content |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113704405B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114969254A (en) * | 2022-05-25 | 2022-08-30 | 北京鼎泰智源科技有限公司 | Automatic news abstract generating method and device |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140257820A1 (en) * | 2013-03-10 | 2014-09-11 | Nice-Systems Ltd | Method and apparatus for real time emotion detection in audio interactions |
| CN106407406A (en) * | 2016-09-22 | 2017-02-15 | 国信优易数据有限公司 | A text processing method and system |
| CN109151218A (en) * | 2018-08-21 | 2019-01-04 | 平安科技（深圳）有限公司 | Call voice quality detecting method, device, computer equipment and storage medium |
| CN109308912A (en) * | 2018-08-02 | 2019-02-05 | 平安科技（深圳）有限公司 | Music style recognition methods, device, computer equipment and storage medium |
| CN109327632A (en) * | 2018-11-23 | 2019-02-12 | 深圳前海微众银行股份有限公司 | Intelligent quality inspection system, method and computer-readable storage medium for customer service recording |
| US20190385711A1 (en) * | 2018-06-19 | 2019-12-19 | Ellipsis Health, Inc. | Systems and methods for mental health assessment |
| CN111666412A (en) * | 2020-06-02 | 2020-09-15 | 国家计算机网络与信息安全管理中心 | Fraud log text analysis method and system based on SVM text analysis |
| CN112527994A (en) * | 2020-12-18 | 2021-03-19 | 平安银行股份有限公司 | Emotion analysis method, emotion analysis device, emotion analysis equipment and readable storage medium |
-
2021
- 2021-08-30 CN CN202111002989.6A patent/CN113704405B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140257820A1 (en) * | 2013-03-10 | 2014-09-11 | Nice-Systems Ltd | Method and apparatus for real time emotion detection in audio interactions |
| CN106407406A (en) * | 2016-09-22 | 2017-02-15 | 国信优易数据有限公司 | A text processing method and system |
| US20190385711A1 (en) * | 2018-06-19 | 2019-12-19 | Ellipsis Health, Inc. | Systems and methods for mental health assessment |
| CN109308912A (en) * | 2018-08-02 | 2019-02-05 | 平安科技（深圳）有限公司 | Music style recognition methods, device, computer equipment and storage medium |
| CN109151218A (en) * | 2018-08-21 | 2019-01-04 | 平安科技（深圳）有限公司 | Call voice quality detecting method, device, computer equipment and storage medium |
| CN109327632A (en) * | 2018-11-23 | 2019-02-12 | 深圳前海微众银行股份有限公司 | Intelligent quality inspection system, method and computer-readable storage medium for customer service recording |
| CN111666412A (en) * | 2020-06-02 | 2020-09-15 | 国家计算机网络与信息安全管理中心 | Fraud log text analysis method and system based on SVM text analysis |
| CN112527994A (en) * | 2020-12-18 | 2021-03-19 | 平安银行股份有限公司 | Emotion analysis method, emotion analysis device, emotion analysis equipment and readable storage medium |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114969254A (en) * | 2022-05-25 | 2022-08-30 | 北京鼎泰智源科技有限公司 | Automatic news abstract generating method and device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113704405B (en) | 2024-06-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112560453B (en) | Voice information verification method and device, electronic equipment and medium | |
| CN112527994A (en) | Emotion analysis method, emotion analysis device, emotion analysis equipment and readable storage medium | |
| CN113722483A (en) | Topic classification method, device, equipment and storage medium | |
| CN112988963B (en) | User intention prediction method, device, equipment and medium based on multi-flow nodes | |
| CN114461777B (en) | Intelligent question-answering method, device, equipment and storage medium | |
| CN114387061A (en) | Product push method, device, electronic device and readable storage medium | |
| CN113064994A (en) | Conference quality evaluation method, device, equipment and storage medium | |
| CN113205814A (en) | Voice data labeling method and device, electronic equipment and storage medium | |
| CN112417128B (en) | Method and device for recommending dialect, computer equipment and storage medium | |
| CN112951233A (en) | Voice question and answer method and device, electronic equipment and readable storage medium | |
| CN113360768A (en) | Product recommendation method, device and equipment based on user portrait and storage medium | |
| CN113268615A (en) | Resource label generation method and device, electronic equipment and storage medium | |
| CN113628043A (en) | Complaint validity judgment method, device, equipment and medium based on data classification | |
| CN114528851B (en) | Reply sentence determination method, reply sentence determination device, electronic equipment and storage medium | |
| CN113344125B (en) | Long text matching recognition method and device, electronic equipment and storage medium | |
| CN113807103A (en) | Recruitment method, device, equipment and storage medium based on artificial intelligence | |
| CN114781402A (en) | Method and device for identifying inquiry intention, electronic equipment and readable storage medium | |
| CN113254814A (en) | Network course video labeling method and device, electronic equipment and medium | |
| CN112712797A (en) | Voice recognition method and device, electronic equipment and readable storage medium | |
| CN112579781A (en) | Text classification method and device, electronic equipment and medium | |
| CN114186028A (en) | Consultation work order processing method, device, equipment and storage medium | |
| CN113808616A (en) | Voice compliance detection method, device, equipment and storage medium | |
| CN113704405B (en) | Quality inspection scoring method, device, equipment and storage medium based on recorded content | |
| CN113870478A (en) | Rapid number-taking method and device, electronic equipment and storage medium | |
| CN113723114A (en) | Semantic analysis method, device and equipment based on multi-intent recognition and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |