CN110047505A - The implementation method and Related product of Quan Yutong neural network based - Google Patents
The implementation method and Related product of Quan Yutong neural network based Download PDFInfo
- Publication number
- CN110047505A CN110047505A CN201910173053.6A CN201910173053A CN110047505A CN 110047505 A CN110047505 A CN 110047505A CN 201910173053 A CN201910173053 A CN 201910173053A CN 110047505 A CN110047505 A CN 110047505A
- Authority
- CN
- China
- Prior art keywords
- language
- voice
- terminal
- neural network
- input
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 34
- 238000013528 artificial neural network Methods 0.000 title claims abstract description 25
- 238000003062 neural network model Methods 0.000 claims abstract description 25
- 238000004422 calculation algorithm Methods 0.000 claims description 29
- 239000011159 matrix material Substances 0.000 claims description 17
- 238000004891 communication Methods 0.000 claims description 12
- 238000004590 computer program Methods 0.000 claims description 10
- 238000012545 processing Methods 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 5
- 239000000284 extract Substances 0.000 claims description 5
- 238000013497 data interchange Methods 0.000 claims description 2
- 210000004218 nerve net Anatomy 0.000 claims 1
- 238000013519 translation Methods 0.000 description 6
- 238000000605 extraction Methods 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 230000008878 coupling Effects 0.000 description 2
- 238000013075 data extraction Methods 0.000 description 2
- 230000005611 electricity Effects 0.000 description 2
- 230000001537 neural effect Effects 0.000 description 2
- 230000001052 transient effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 210000004556 brain Anatomy 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007717 exclusion Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 210000005036 nerve Anatomy 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/40—Processing or translation of natural language
- G06F40/58—Use of machine translation, e.g. for multi-lingual retrieval, for server-side translation for client devices or for real-time translation
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/27—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the analysis technique
- G10L25/30—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the analysis technique using neural networks
Abstract
This application provides the implementation methods of Quan Yutong neural network based a kind of, this method comprises: terminal receives the first voice of the first language of user, second language needed for determining terminal call；First language and second language are sent to network side by terminal, receive the first parameter of the neural network model that network side issues, and the first parameter is loaded onto neural network model by terminal；First voice is input to neural network model execution multilayer neural network operation and obtains the second voice for meeting second language by terminal, and the second voice is sent to communicating end by network.Technical solution provided by the present application has the advantages that user experience is high.
Description
Technical field
This application involves communication and field of terminal, and in particular to a kind of implementation method of Quan Yutong neural network based
And Related product.
Background technique
Terminal, such as tablet computer, smart phone etc..Here by taking smart phone as an example, smart phone refers to as personal electricity
Brain is the same, has independent operating system, and independent running space can voluntarily be installed software, game, navigation etc. the by user
The program that tripartite service provider provides, and the general name of wireless network access type of cell phone can be realized by mobile communication network.
The call of current smart phone is only merely the forwarding of call, such as Zhang San and Li Si converse, the Chinese of Zhang San
Voice is directly forwarded to Li Si, if Li Si is American, Li Si is needed to understand that Chinese can be understood, conversely, needing
Zhang San needs English to exchange with Li Si, and existing translation is only limited to the translation fixed for Chinese-English etc., and existing AI is turned over
It cannot achieve corresponding translation if translating device and being replaced with the translation of Chinese-French, so it cannot achieve Quan Yutong, user's body
Degree of testing is low.
Apply for content
The embodiment of the present application provides the implementation method and Related product of a kind of Quan Yutong neural network based, realizes language
The Quan Yutong of sound, an AI translater can be realized the intercommunication between any bilingual, improve user experience.
In a first aspect, the embodiment of the present application provides the implementation method of Quan Yutong neural network based a kind of, the method
Include the following steps:
Terminal receives the first voice of the first language of user, second language needed for determining terminal call；
First language and second language are sent to network side by terminal, receive the neural network model that network side issues
First parameter is loaded onto neural network model by the first parameter, terminal；
First voice is input to neural network model execution multilayer neural network operation and obtains meeting second language by terminal
The second voice, the second voice is sent to communicating end by network.
Optionally, the first voice is input to neural network model execution multilayer neural network operation and is accorded with by the terminal
The second voice for closing second language specifically includes:
Terminal obtains input data according to the first voice, and input data is input to multilayer neural network and executes n-layer convolution
Operation obtains convolution algorithm as a result, convolution algorithm result, which is input to full connection operation, obtains the second voice of the second voice.
Optionally, the first layer convolution algorithm executed in n-layer convolution algorithm specifically includes:
It determines input data matrix [H] [W], the convolution kernel [CI] [3] [3] of first layer convolution algorithm, by input matrix [H]
[W] is divided into H/3 data block, and element value presses the adjacent storage in the direction H in each data block in the H/3 data block, will roll up
Product core [CI] [3] [3] is divided into CI [3] [3] core blocks, and the element value of each core block is adjacent in the CI [3] [3] core blocks deposits
Input data matrix [H] [W] and convolution kernel [CI] [3] [3] is executed convolution algorithm and obtains first layer convolutional calculation result by storage.
Optionally, second language needed for the determining terminal call can specifically include:
Terminal extracts the user of communicating end, requests the corresponding nationality of the user to network side, receives being somebody's turn to do for network side return
The nationality of user registration determines that the corresponding mother tongue of the nationality is second language.
Second aspect, provides a kind of terminal, and the terminal includes:
Audio unit, the first voice of the first language for receiving user；
Processing unit, for second language needed for determining terminal call；Communication unit is controlled by first language and the
Two language are sent to network side, control the first parameter that the communication unit receives the neural network model that network side issues, will
First parameter is loaded onto neural network model；First voice is input to neural network model execution multilayer neural network operation to obtain
To the second voice for meeting second language, the communication unit is controlled by the second voice, communicating end is sent to by network.
Optionally, input data is input to by the processing unit specifically for obtaining input data according to the first voice
Multilayer neural network execution n-layer convolution algorithm obtains convolution algorithm and obtains as a result, convolution algorithm result is input to full connection operation
To the second voice of the second voice.
Optionally, the processing unit is specifically used for determining input data matrix [H] [W], the volume of first layer convolution algorithm
Product core [CI] [3] [3], is divided into H/3 data block, each data block in the H/3 data block for input matrix [H] [W]
Middle element value presses the adjacent storage in the direction H, convolution kernel [CI] [3] [3] is divided into CI [3] [3] core blocks, the CI is a [3] [3]
Input data matrix [H] [W] and convolution kernel [CI] [3] [3] is executed convolution by the adjacent storage of element value of each core block in core block
Operation obtains first layer convolutional calculation result.
Optionally, the processing unit requests the user corresponding specifically for extracting the user of communicating end to network side
Nationality receives the nationality for the user registration that network side returns, and determines that the corresponding mother tongue of the nationality is second language.
Optionally, the terminal are as follows: smart phone or tablet computer.
The third aspect, provides a kind of computer readable storage medium, and storage is used for the computer journey of electronic data interchange
Sequence, wherein the computer program makes computer execute the method that first aspect provides.
Fourth aspect, provides a kind of computer program product, and the computer program product includes storing computer journey
The non-transient computer readable storage medium of sequence, the computer program are operable to that computer is made to execute first aspect offer
Method.
Implement the embodiment of the present application, has the following beneficial effects:
As can be seen that technical solution provided by the present application after receiving the first voice of first language, determines the second language
Then speech determines that first language to the first parameter of second language, loads the parameter to neural network model, in this way from network side
It can realize the translation to first language to second language, and since the parameter in the application is due to temporarily loading,
Therefore it is loaded directly into new parameter when second language is replaced, and can realize that single Neural model is suitable in this way
Ying Quanyu logical technical solution, improves user experience.
Detailed description of the invention
In order to more clearly explain the technical solutions in the embodiments of the present application, make required in being described below to embodiment
Attached drawing is briefly described, it should be apparent that, the accompanying drawings in the following description is some embodiments of the present application, for ability
For the those of ordinary skill of domain, without creative efforts, it can also be obtained according to these attached drawings other attached
Figure.
Fig. 1 is a kind of structural schematic diagram of computing device provided by the embodiments of the present application.
Fig. 2 is a kind of process signal of the implementation method of Quan Yutong neural network based disclosed in the embodiment of the present application
Figure.
Fig. 3 is a kind of schematic diagram of terminal provided by the embodiments of the present application.
Specific embodiment
Below in conjunction with the attached drawing in the embodiment of the present application, technical solutions in the embodiments of the present application carries out clear, complete
Site preparation description, it is clear that described embodiment is some embodiments of the present application, instead of all the embodiments.Based on this Shen
Please in embodiment, every other implementation obtained by those of ordinary skill in the art without making creative efforts
Example, shall fall in the protection scope of this application.
The description and claims of this application and term " first ", " second ", " third " and " in the attached drawing
Four " etc. are not use to describe a particular order for distinguishing different objects.In addition, term " includes " and " having " and it
Any deformation, it is intended that cover and non-exclusive include.Such as it contains the process, method of a series of steps or units, be
System, product or equipment are not limited to listed step or unit, but optionally further comprising the step of not listing or list
Member, or optionally further comprising other step or units intrinsic for these process, methods, product or equipment.
Referenced herein " embodiment " is it is meant that a particular feature, structure, or characteristic described can wrap in conjunction with the embodiments
It is contained at least one embodiment of the application.Each position in the description occur the phrase might not each mean it is identical
Embodiment, nor the independent or alternative embodiment with other embodiments mutual exclusion.Those skilled in the art explicitly and
Implicitly understand, embodiment described herein can be combined with other embodiments.
Refering to fig. 1, Fig. 1 be a kind of terminal structural schematic diagram, as shown in Figure 1, the terminal may include: processor 101,
Memory 102, display screen 103, acoustic component 104, wherein processor 101 by bus and memory 102, display screen 103,
Audio frequency apparatus 104 connects.Above-mentioned acoustic component can be microphone, naturally it is also possible to include headset.
The implementation method of Quan Yutong neural network based provided by the present application a kind of, this method use as shown in Figure 1
Terminal realizes that this method is as shown in Fig. 2, include the following steps:
Step S201, terminal receives the first voice of the first language of user, second language needed for determining terminal call；
Step S202, first language and second language are sent to network side by terminal, receive the nerve that network side issues
First parameter is loaded onto neural network model by the first parameter of network model, terminal；
Step S203, the first voice is input to neural network model execution multilayer neural network operation and is met by terminal
Second voice is sent to communicating end by network by the second voice of second language.
Technical solution provided by the present application determines second language after receiving the first voice of first language, then from
Network side determines first language to the first parameter of second language, loads the parameter to neural network model, in this way can be real
Now to the translation of first language to second language, and since the parameter in the application is due to temporarily loading,
When second language is replaced, it is loaded directly into new parameter, can realize that single Neural model adapts to Quan Yutong in this way
Technical solution, improve user experience.
Optionally, the first voice is input to neural network model execution multilayer neural network operation and is accorded with by above-mentioned terminal
The second voice for closing second language specifically includes:
Terminal obtains input data according to the first voice, input data is input to multilayer neural network executes n and (be greater than etc.
In 2 integer) layer convolution algorithm obtains convolution algorithm as a result, convolution algorithm result, which is input to full connection operation, obtains the second language
Second voice of sound.
Optionally, the first layer convolution algorithm in above-mentioned execution n-layer convolution algorithm can specifically include:
It determines input data matrix [H] [W], the convolution kernel [CI] [3] [3] of first layer convolution algorithm, by input matrix [H]
[W] is divided into H/3 data block, and element value presses the adjacent storage in the direction H in each data block in the H/3 data block, will roll up
Product core [CI] [3] [3] is divided into CI [3] [3] core blocks, and the element value of each core block is adjacent in the CI [3] [3] core blocks deposits
Input data matrix [H] [W] and convolution kernel [CI] [3] [3] is executed convolution algorithm and obtains first layer convolutional calculation result by storage.
[H] in above-mentioned input data matrix [H] [W] indicates short transverse value, and [W] indicates width direction value, convolution kernel
[3] [3] in [CI] [3] [3] indicate that the basic convolution kernel of 3*3, [CI] indicate the depth value of convolution kernel.
Above scheme mainly arranges the sequence of storage, in this way the speed of raising internal storage data extraction, for interior
For the extraction deposited, the data extracted every time are 128bit data, and individual element value is 16 bits or 8bit at present, if not
Input data matrix [H] [W] is divided, then when extracting data, due to the convolution kernel that extraction is one [3] [3],
The direction H and the direction W all have corresponding data, by taking 16 bits as an example, in this way for its needs of the convolution kernel of one [3] [3]
The number of extraction is 3 times, i.e., extracts 8 element values every time, but abandons 5 element values, but for the technical side of the application
Case need to only be extracted 2 times, i.e., extract 8 element values for the first time and be needed, second of 8 element values first extracted
Element value needs, behind 7 element values abandon, which reduces the numbers that a data are extracted, the extraction for convolution kernel
Also the efficiency that can be improved data extraction, improves the speed of convolution algorithm in this way.
Second language needed for above-mentioned determining terminal call can specifically include:
Terminal extracts the user of communicating end, requests the corresponding nationality of the user to network side, receives being somebody's turn to do for network side return
The nationality of user registration determines that the corresponding mother tongue of the nationality is second language.
A kind of terminal is provided refering to Fig. 3, Fig. 3, the terminal includes:
Audio unit, the first voice of the first language for receiving user；
Processing unit, for second language needed for determining terminal call；Communication unit is controlled by first language and the
Two language are sent to network side, control the first parameter that the communication unit receives the neural network model that network side issues, will
First parameter is loaded onto neural network model；First voice is input to neural network model execution multilayer neural network operation to obtain
To the second voice for meeting second language, the communication unit is controlled by the second voice, communicating end is sent to by network.
Above-mentioned terminal is specifically as follows smart phone or tablet computer.
The embodiment of the present application also provides a kind of computer storage medium, wherein computer storage medium storage is for electricity
The computer program of subdata exchange, it is as any in recorded in above method embodiment which execute computer
A kind of some or all of the implementation method of Quan Yutong neural network based step.
The embodiment of the present application also provides a kind of computer program product, and the computer program product includes storing calculating
The non-transient computer readable storage medium of machine program, the computer program are operable to that computer is made to execute such as above-mentioned side
Some or all of the implementation method of any one Quan Yutong neural network based recorded in method embodiment step.
It should be noted that for the various method embodiments described above, for simple description, therefore, it is stated as a series of
Combination of actions, but those skilled in the art should understand that, the application is not limited by the described action sequence because
According to the application, some steps may be performed in other sequences or simultaneously.Secondly, those skilled in the art should also know
It knows, embodiment described in this description belongs to alternative embodiment, related actions and modules not necessarily the application
It is necessary.
In the above-described embodiments, it all emphasizes particularly on different fields to the description of each embodiment, there is no the portion being described in detail in some embodiment
Point, reference can be made to the related descriptions of other embodiments.
In several embodiments provided herein, it should be understood that disclosed device, it can be by another way
It realizes.For example, the apparatus embodiments described above are merely exemplary, such as the division of the unit, it is only a kind of
Logical function partition, there may be another division manner in actual implementation, such as multiple units or components can combine or can
To be integrated into another system, or some features can be ignored or not executed.Another point, shown or discussed is mutual
Coupling, direct-coupling or communication connection can be through some interfaces, the indirect coupling or communication connection of device or unit,
It can be electrical or other forms.
The unit as illustrated by the separation member may or may not be physically separated, aobvious as unit
The component shown may or may not be physical unit, it can and it is in one place, or may be distributed over multiple
In network unit.It can select some or all of unit therein according to the actual needs to realize the mesh of this embodiment scheme
's.
It, can also be in addition, each functional unit in each embodiment of the application can integrate in one processing unit
It is that each unit physically exists alone, can also be integrated in one unit with two or more units.Above-mentioned integrated list
Member both can take the form of hardware realization, can also be realized in the form of software program module.
If the integrated unit is realized in the form of software program module and sells or use as independent product
When, it can store in a computer-readable access to memory.Based on this understanding, the technical solution of the application substantially or
Person says that all or part of the part that contributes to existing technology or the technical solution can body in the form of software products
Reveal and, which is stored in a memory, including some instructions are used so that a computer equipment
(can be personal computer, server or network equipment etc.) executes all or part of each embodiment the method for the application
Step.And memory above-mentioned includes: USB flash disk, read-only memory (ROM, Read-Only Memory), random access memory
The various media that can store program code such as (RAM, Random Access Memory), mobile hard disk, magnetic or disk.
Those of ordinary skill in the art will appreciate that all or part of the steps in the various methods of above-described embodiment is can
It is completed with instructing relevant hardware by program, which can store in a computer-readable memory, memory
May include: flash disk, read-only memory (English: Read-Only Memory, referred to as: ROM), random access device (English:
Random Access Memory, referred to as: RAM), disk or CD etc..
The embodiment of the present application is described in detail above, specific case used herein to the principle of the application and
Embodiment is expounded, the description of the example is only used to help understand the method for the present application and its core ideas；
At the same time, for those skilled in the art can in specific embodiments and applications according to the thought of the application
There is change place, in conclusion the contents of this specification should not be construed as limiting the present application.
Claims (10)
1. a kind of implementation method of Quan Yutong neural network based, which is characterized in that described method includes following steps:
Terminal receives the first voice of the first language of user, second language needed for determining terminal call；
First language and second language are sent to network side by terminal, receive the first of the neural network model that network side issues
First parameter is loaded onto neural network model by parameter, terminal；
Terminal the first voice is input to that neural network model executes that multilayer neural network operation obtains meeting second language the
Second voice is sent to communicating end by network by two voices.
2. the method according to claim 1, wherein the first voice is input to neural network model by the terminal
The second voice that execution multilayer neural network operation obtains meeting second language specifically includes:
Terminal obtains input data according to the first voice, and input data is input to multilayer neural network and executes n-layer convolution algorithm
Convolution algorithm is obtained as a result, convolution algorithm result, which is input to full connection operation, obtains the second voice of the second voice.
3. according to the method described in claim 2, it is characterized in that, the first layer convolution fortune executed in n-layer convolution algorithm
Calculator body includes:
It determines input data matrix [H] [W], the convolution kernel [CI] [3] [3] of first layer convolution algorithm, by input matrix [H] [W]
It is divided into H/3 data block, element value presses the adjacent storage in the direction H in each data block in the H/3 data block, by convolution kernel
[CI] [3] [3] are divided into CI [3] [3] core blocks, the adjacent storage of element value of each core block in the CI [3] [3] core blocks,
Input data matrix [H] [W] and convolution kernel [CI] [3] [3] is executed into convolution algorithm and obtains first layer convolutional calculation result.
4. the method according to claim 1, wherein second language needed for the determining terminal call specifically may be used
To include:
Terminal extracts the user of communicating end, requests the corresponding nationality of the user to network side, receives the user that network side returns
The nationality of registration determines that the corresponding mother tongue of the nationality is second language.
5. a kind of terminal, which is characterized in that the terminal includes:
Audio unit, the first voice of the first language for receiving user；
Processing unit, for second language needed for determining terminal call；Communication unit is controlled by first language and the second language
Speech is sent to network side, the first parameter that the communication unit receives the neural network model that network side issues is controlled, by first
Parameter is loaded onto neural network model；First voice is input to neural network model execution multilayer neural network operation to be accorded with
The second voice for closing second language, controls the communication unit for the second voice and is sent to communicating end by network.
6. terminal according to claim 5, which is characterized in that
Input data is input to multilayer nerve net specifically for obtaining input data according to the first voice by the processing unit
Network executes n-layer convolution algorithm and obtains convolution algorithm as a result, convolution algorithm result, which is input to full connection operation, obtains the second voice
The second voice.
7. terminal according to claim 6, which is characterized in that
The processing unit is specifically used for determining input data matrix [H] [W], the convolution kernel [CI] [3] of first layer convolution algorithm
[3], input matrix [H] [W] is divided into H/3 data block, element value presses H in each data block in the H/3 data block
Convolution kernel [CI] [3] [3] is divided into CI [3] [3] core blocks by the adjacent storage in direction, each in the CI [3] [3] core blocks
Input data matrix [H] [W] and convolution kernel [CI] [3] [3] are executed convolution algorithm and obtain the by the adjacent storage of the element value of core block
One layer of convolutional calculation result.
8. terminal according to claim 5, which is characterized in that
The processing unit requests the corresponding nationality of the user to network side, receives net specifically for extracting the user of communicating end
The nationality for the user registration that network side returns determines that the corresponding mother tongue of the nationality is second language.
9. according to terminal described in claim 5-8 any one, which is characterized in that
The terminal are as follows: smart phone or tablet computer.
10. a kind of computer readable storage medium, which is characterized in that it stores the computer program for being used for electronic data interchange,
Wherein, the computer program makes computer execute the method as described in claim 1-4 any one.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910173053.6A CN110047505A (en) | 2019-03-07 | 2019-03-07 | The implementation method and Related product of Quan Yutong neural network based |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910173053.6A CN110047505A (en) | 2019-03-07 | 2019-03-07 | The implementation method and Related product of Quan Yutong neural network based |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110047505A true CN110047505A (en) | 2019-07-23 |
Family
ID=67274622
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910173053.6A Pending CN110047505A (en) | 2019-03-07 | 2019-03-07 | The implementation method and Related product of Quan Yutong neural network based |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110047505A (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107783966A (en) * | 2017-10-31 | 2018-03-09 | 珠海市魅族科技有限公司 | Communication control method, device, computer installation and computer-readable recording medium |
| CN108268931A (en) * | 2016-12-30 | 2018-07-10 | 华为技术有限公司 | The methods, devices and systems of data processing |
| CN108628836A (en) * | 2017-03-22 | 2018-10-09 | 湖南本来文化发展有限公司 | The robot of voiced translation is carried out using artificial intelligence BP neural network algorithm |
| CN109088995A (en) * | 2018-10-17 | 2018-12-25 | 永德利硅橡胶科技(深圳)有限公司 | Support the method and mobile phone of global languages translation |
| CN109286725A (en) * | 2018-10-15 | 2019-01-29 | 华为技术有限公司 | Interpretation method and terminal |
| CN109327614A (en) * | 2018-10-17 | 2019-02-12 | 永德利硅橡胶科技(深圳)有限公司 | Global simultaneous interpretation mobile phone and method |
-
2019
- 2019-03-07 CN CN201910173053.6A patent/CN110047505A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108268931A (en) * | 2016-12-30 | 2018-07-10 | 华为技术有限公司 | The methods, devices and systems of data processing |
| CN108628836A (en) * | 2017-03-22 | 2018-10-09 | 湖南本来文化发展有限公司 | The robot of voiced translation is carried out using artificial intelligence BP neural network algorithm |
| CN107783966A (en) * | 2017-10-31 | 2018-03-09 | 珠海市魅族科技有限公司 | Communication control method, device, computer installation and computer-readable recording medium |
| CN109286725A (en) * | 2018-10-15 | 2019-01-29 | 华为技术有限公司 | Interpretation method and terminal |
| CN109088995A (en) * | 2018-10-17 | 2018-12-25 | 永德利硅橡胶科技(深圳)有限公司 | Support the method and mobile phone of global languages translation |
| CN109327614A (en) * | 2018-10-17 | 2019-02-12 | 永德利硅橡胶科技(深圳)有限公司 | Global simultaneous interpretation mobile phone and method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109088995A (en) | Support the method and mobile phone of global languages translation | |
| CN107733984A (en) | A kind of method, terminal and computer-readable recording medium for pushing screen locking information | |
| CN110147249A (en) | A kind of calculation method and device of network model | |
| CN109327614A (en) | Global simultaneous interpretation mobile phone and method | |
| CN111210004B (en) | Convolution calculation method, convolution calculation device and terminal equipment | |
| CN105159927B (en) | Method and device for selecting subject term of target text and terminal | |
| CN110955390A (en) | Data processing method and device and electronic equipment | |
| CN116842307B (en) | Data processing method, device, equipment, chip and storage medium | |
| CN109542878A (en) | A kind of list creation techniques and device | |
| CN108650306A (en) | A kind of game video caching method, device and computer storage media | |
| CN110047505A (en) | The implementation method and Related product of Quan Yutong neural network based | |
| CN108551485A (en) | A kind of streaming medium content caching method, device and computer storage media | |
| CN107169115A (en) | Add the method and device of self-defined participle | |
| CN110309328A (en) | Date storage method, device, electronic equipment and storage medium | |
| CN105551500B (en) | A kind of acoustic signal processing method and device | |
| CN108958699A (en) | Voice pick-up method and Related product | |
| CN108540569A (en) | A kind of software installation packet replacement method, device and computer storage media | |
| CN110049200A (en) | Interaction language translating method and Related product based on meeting | |
| CN110032743A (en) | The implementation method and Related product of the Quan Yutong of multi-player mode | |
| CN106297812A (en) | A kind of data processing method and terminal | |
| CN103618996B (en) | Position data processing method, client and server | |
| CN108574853A (en) | A kind of content of TV program caching method, device and computer storage media | |
| CN113051126A (en) | Image construction method, device and equipment and storage medium | |
| CN111163123A (en) | Service request processing method and device | |
| CN109697246A (en) | The concern people video recommendation method and Related product of short-sighted frequency |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190723 |
|
| RJ01 | Rejection of invention patent application after publication |