CN108156317A - call voice control method, device and storage medium and mobile terminal - Google Patents
call voice control method, device and storage medium and mobile terminal Download PDFInfo
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Classifications
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M1/00—Substation equipment, e.g. for use by subscribers
- H04M1/72—Mobile telephones; Cordless telephones, i.e. devices for establishing wireless links to base stations without route selection
- H04M1/724—User interfaces specially adapted for cordless or mobile telephones
- H04M1/72448—User interfaces specially adapted for cordless or mobile telephones with means for adapting the functionality of the device according to specific conditions
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/06—Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice
- G10L15/063—Training
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/06—Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice
- G10L15/065—Adaptation
- G10L15/07—Adaptation to the speaker
- G10L15/075—Adaptation to the speaker supervised, i.e. under machine guidance
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- 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
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use
- G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination
- G10L25/66—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination for extracting parameters related to health condition
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M1/00—Substation equipment, e.g. for use by subscribers
- H04M1/72—Mobile telephones; Cordless telephones, i.e. devices for establishing wireless links to base stations without route selection
- H04M1/724—User interfaces specially adapted for cordless or mobile telephones
- H04M1/72484—User interfaces specially adapted for cordless or mobile telephones wherein functions are triggered by incoming communication events
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Abstract
The embodiment of the present application discloses a kind of call voice control method, device and storage medium and mobile terminal, the method includes：When detecting that current mobile terminal is in call mode, the contact type of current talking contact person is obtained；Obtain the default feedback model generated based on machine learning method；Contact type is input in default feedback model, obtains the destination call sound characteristic of default feedback model output；The call voice of current mobile terminal user is adjusted according to destination call sound characteristic, terminal where the call voice after adjustment is sent to the current talking contact person.Technical solution provided by the embodiments of the present application, it realizes and in due course adjustment is carried out according to call contact person type to user's communication voice, no matter which type of the call voice that user sends out is, the user's communication voice sound characteristic voice of all conversing for being sent to terminal where call contact person matches with current talking contact person, also improves the interest of voice communication.
Description
Technical field
The invention relates to call control technology field more particularly to a kind of call voice control method, device and
Storage medium and mobile terminal.
Background technology
Function in the mobile terminals such as mobile phone is more and more, and the live and work for people is provided convenience, voice communication
Function is a basic function in mobile phone, and people can take phone, receiving and transmitting voice message using mobile phone.Hand is used in user
The process of machine voice communication to call voice control method existing defects in the relevant technologies, needs to improve.
Invention content
The embodiment of the present application provides a kind of call voice control method, device and storage medium and mobile terminal, Ke Yiyou
Change the control program of call voice.
In a first aspect, the embodiment of the present application provides a kind of call voice control method, including：
When detecting that current mobile terminal is in call mode, the contact type of current talking contact person is obtained；
The default feedback model generated based on machine learning method is obtained, the default feedback model is by multiple known users
The call-information sample training of call sound characteristic obtains, for based on call contact person type feedback dialogue call contact person
User's communication sound characteristic；
The contact type is input in the default feedback model, obtains the mesh of the default feedback model output
Mark call sound characteristic；
The call voice of current mobile terminal user is adjusted according to the destination call sound characteristic, after adjustment
Call voice be sent to terminal where the current talking contact person.
In second aspect, the embodiment of the present application provides a kind of call voice control device, including：
Contact type acquisition module is current logical for when detecting that current mobile terminal is in call mode, obtaining
Talk about the contact type of contact person；
Default feedback model acquisition module, it is described for obtaining the default feedback model generated based on machine learning method
Default feedback model is obtained by the call-information sample training of multiple known users call sound characteristic information, for being based on conversing
The user's communication sound characteristic of contact type feedback dialogue call contact person；
Destination call sound characteristic acquisition module, for the contact type to be input to the default feedback model
In, obtain the destination call sound characteristic that the default feedback model exports；
Call voice adjust module, for according to the destination call sound characteristic information to current mobile terminal user's
Call voice is adjusted, terminal where the call voice after adjustment is sent to current talking contact person.
In the third aspect, the embodiment of the present application provides a kind of computer readable storage medium, is stored thereon with computer
Program realizes the call voice control method provided such as first aspect when the program is executed by processor.
In fourth aspect, the embodiment of the present application provides a kind of mobile terminal, including memory, processor and is stored in
On reservoir and the computer program that can run on a processor, realized when the processor performs as what first aspect was provided leads to
Language sound controlling method.
The embodiment of the present application by be generated in advance on mobile terminal or server one for determine be suitble to dialogue lead to
The default feedback model of the user's communication sound characteristic of contact person is talked about, when mobile terminal is under call mode, is obtained current
Contact type is input in default feedback model by the contact type of call contact person, obtains being suitble to current talking contact
The destination call sound characteristic of the current mobile terminal user of people, according to destination call sound characteristic to the call voice of user into
Call voice after adjustment is sent to terminal where current talking contact person by row adjustment, realize to user's communication voice by
In due course adjustment is carried out according to call contact person type, no matter which type of the call voice that user sends out is, is sent to call connection
It is that the user's communication voice sound characteristic voice of all conversing of terminal where people matches with current talking contact person, is also promoted
The interest of voice communication.
Description of the drawings
Fig. 1 is a kind of flow chart of call voice control method provided by the embodiments of the present application；
Fig. 2 is the flow chart of another call voice control method provided by the embodiments of the present application；
Fig. 3 is a kind of structure diagram of call voice control device provided by the embodiments of the present application；
Fig. 4 is a kind of structure diagram of mobile terminal provided by the embodiments of the present application；
Fig. 5 is the structure diagram of another mobile terminal provided by the embodiments of the present application.
Specific embodiment
It is specifically real to the application below in conjunction with the accompanying drawings in order to make the purpose, technical scheme and advantage of the application clearer
Example is applied to be described in further detail.It is understood that specific embodiment described herein is used only for explaining the application,
Rather than the restriction to the application.It also should be noted that it illustrates only for ease of description, in attached drawing related to the application
Part rather than full content.It should be mentioned that some exemplary realities before exemplary embodiment is discussed in greater detail
It applies example and is described as the processing described as flow chart or method.Although operations (or step) are described as sequence by flow chart
Processing, but many of which operation can be implemented concurrently, concomitantly or simultaneously.In addition, the sequence of operations
It can be rearranged.The processing can be terminated when its operations are completed, be not included in attached drawing it is also possible to have
Additional step.The processing can correspond to method, function, regulation, subroutine, subprogram etc..
Fig. 1 gives a kind of flow chart of call voice control method provided by the embodiments of the present application, the side of the present embodiment
Method can be performed by call voice control device, which can realize that described device can by way of hardware and/or software
The inside of the mobile terminal is arranged on as a mobile terminal part.Mobile terminal described in the present embodiment includes but unlimited
There is the equipment of call function due to smart mobile phone, tablet computer or notebook etc..
As shown in Figure 1, call voice control method provided in this embodiment includes the following steps：
Step 101, when detecting that current mobile terminal is in call mode, obtain current talking contact person contact person
Type.
Call mode described in the present embodiment includes telephone calling model, third party's voice communication software is conversed (for example,
Videos/the voice communication such as wechat, QQ, wechat speech message are sent out) pattern or other call modes.
Assuming that the user of current mobile terminal is A, user A converses with call contact person B, the call contact person
The relationship of B and user A include many kinds, which is identified by contact type, wherein, contact type can include colleague,
Leader, parent, relative, friend, client, lover or sales force.
The default feedback model that step 102, acquisition are generated based on machine learning method, the default feedback model is by multiple
The call-information sample training of known users call sound characteristic obtains, for being based on the dialogue call of call contact person type feedback
The user's communication sound characteristic of contact person.
The training generation of the default feedback model based on machine learning method and renewal process can be in mobile terminals
It is local to carry out, it can also be carried out in predetermined server, it, can after the training generation of default feedback model is finished or updated
It is stored or is stored in predetermined server to be sent directly to mobile terminal, wait standby communication terminals active obtaining.Accordingly
, which can include：From predetermined server or mobile terminal locally obtains what is generated based on machine learning method
Default feedback model.Wherein, machine learning method includes neural network method, support vector machine method, traditional decision-tree, logic
Homing method, bayes method and random forest method.
In the present embodiment, the source and quantity of the call-information sample of known users call sound characteristic are not done
It is specific to limit.For example, training sample can be the history call-information of the mobile terminal user or target user group
History call-information, the target user group can be with mobile terminal user have same subscriber attribute multiple users,
User property includes age, gender, hobby, occupation and usually sound of speaking feature.It is understood that for being based on engineering
For the model of habit, the quantity of general sample is bigger, and the output result of model is more accurate.
Call-information includes the contact type of call contact person and user is embodied when talking with call contact person
Call sound characteristic out.Generally, a user sends out when the call contact person from different contact types is conversed
Sound be different, for example, when conversing with leader or client, general sound is all relatively more formal, with parent, relative or friend
During friend's call, sound is generally all relatively more normal, and when conversing with sales force, sound is generally all stronger.It is applied in another kind
Under scene, such as mobile terminal user requires higher occupation to use call voice for customer service operator or sales force etc.
During family, when call contact person type is client and being other types, the call sound characteristic of the mobile terminal user may
There is significant difference.Call-information includes the message registration of a plurality of user and each call contact person, and each is led to
Words record mark call contact person type and user's communication sound characteristic.Wherein, user's communication sound characteristic can conversed
The sound characteristic information of the call voice is extracted in journey according to the call voice data waveform of user.
Using the call contact person type in history call-information as the input of default feedback model, in history call-information
Output of the user's communication sound characteristic as default feedback model, the history call-information sample is trained, is generated
Default feedback model.Feedback model is preset for this, when subsequent movement terminal is under call mode, by by current talking
Contact type belonging to contact person is input to default feedback model, you can feedback output user is led to current talking contact person
During words, the call sound characteristic that should have that is predicted.
Wherein, call sound characteristic includes at least one in tone color, tone, loudness, the tone, word speed and tongue.
The call sound characteristic can be determined according to waveform shape, vibration frequency and the Oscillation Amplitude in call voice data waveform.
Wherein, the default feedback model can have multiple, and some is used to feed back tamber characteristic, some user feedback tones
Feature, some is for feeding back loudness feature, and for feeding back the tone, word speed feature, some is used to feed back tongue feature some,
After tone color, tone, loudness, the tone, word speed and tongue is respectively obtained based on each default feedback model, melted
It closes, obtains final complete call sound characteristic, wherein, each default feedback model can be given birth to based on different machine learning methods
Into.It can also obtain the call of tone color, tone, loudness, the tone, word speed and tongue simultaneously based on a default feedback model
Sound characteristic.
The contact type is input in the default feedback model by step 103, obtains the default feedback model
The destination call sound characteristic of output.
The contact type of current talking contact person is input in the default feedback model, obtains default feedback model
The destination call sound characteristic of output, the destination call sound characteristic are talked with for mobile terminal user and current talking contact person
The sound characteristic that Shi Suoying has.
Step 104 is adjusted the call voice of current mobile terminal user according to the destination call sound characteristic,
Terminal where call voice after adjustment is sent to the current talking contact person.
In communication process, call voice data that mobile terminal microphone user in real is sent out are conversed by this
Voice data is sent to before call contact person, and modification is adjusted to call voice based on destination call sound characteristic, will be adjusted
Call voice after whole is sent to terminal where the current talking contact person.
Illustratively, mobile terminal user is customer service operator, and current talking contacts artificial client, in sometimes client
Operator is likely to be at the sick state of flu, and sound of inevitably conversing meets occupation call requirement without usually melodious, then if opening
The call voice control switch of mobile terminal is opened, mobile terminal automatically can meet conversation client contact according to what is acquired
The call sound characteristic of people is adjusted modification to the call voice of customer service operator, can eliminate or cover customer service words
The voice messaging of the sick state of business person so that mobile terminal is more bonded user demand to the control of call voice, improves language
The interest of sound call.
Call voice control method provided in this embodiment, by the way that one is generated in advance on mobile terminal or server
For determining the default feedback model for the user's communication sound characteristic for being suitble to dialogue call contact person, call is in mobile terminal
When under pattern, the contact type of current talking contact person is obtained, contact type is input in default feedback model, is obtained
It is suitble to the destination call sound characteristic of the current mobile terminal user of current talking contact person, according to destination call sound characteristic pair
The call voice of user is adjusted, and terminal where the call voice after adjustment is sent to current talking contact person realizes
In due course adjustment is carried out according to call contact person type to user's communication voice, no matter what the call voice that user sends out is
, the user's communication voice of terminal where being sent to call contact person is all call sound characteristic voice and current talking contact person
Match, also improve the interest of voice communication.
Below by taking machine learning method is neural network method as an example, to default anti-using being generated by neural network method
Model is presented, the method for carrying out call voice control is briefly described.Fig. 2 gives provided by the embodiments of the present application another logical
The flow chart of language sound controlling method.As shown in Fig. 2, method provided in this embodiment includes the following steps：
Step 201 locally obtains the history call-information of mobile terminal user from mobile terminal or from predetermined server
The middle history call-information for obtaining target user group, as history call-information sample.
Step 202 is trained the history call-information sample using neural network method, generates default feedback mould
Type.
The step can be trained the history call-information sample including the use of depth autocoder, and generation is pre-
If feedback model.
Step 203, when detecting that current mobile terminal is in call mode, obtain current talking contact person contact person
Type.
The contact type is input in the default feedback model by step 204, obtains the default feedback model
The destination call sound characteristic of output.
Step 205 is adjusted the call voice of current mobile terminal user according to the destination call sound characteristic,
Terminal where call voice after adjustment is sent to the current talking contact person.
Based on the above technical solution, the neural network method includes input layer, hidden layer and output layer；Step
202 can include：The contact type of each call contact person in the history call-information is input to the input layer, and
By the calculating of activation primitive corresponding with each node of the hidden layer, output intermediate user call sound characteristic；Using described
Talk with the user's communication sound characteristic of each call contact person in intermediate user call sound characteristic and the history call-information
Between difference and optimization algorithm the weight in the activation primitive is corrected repeatedly, until the intermediate user leads to
The difference between sound characteristic and the user's communication sound characteristic is talked about within preset range, obtains each section of training completion
The activation primitive of point generates default feedback model.
Neural network (Neural Networks, be abbreviated as NNs) system refers to artificial neural network, inspires from the mankind
Brain handles the biological neural network of information, it includes input layer, hidden layer and output layer, corresponding to include three kinds of nodes (god
Basic unit through network)：Input node, concealed nodes and output node, input node obtain information from the external world；It hides
Node and the external world do not contact directly, these nodes are calculated using activation primitive, and information is passed from input node
It is delivered to output node；Output node is used to transmit information to the external world.
Wherein, the activation primitive refers to provide Nonlinear Modeling ability for nerve network system, it is however generally that is non-thread
Property function.Activation primitive can include relu functions, sigmoid functions, tanh functions or maxout functions.
Sigmoid is common nonlinear activation primitive, its mathematical form is as follows：It
Export the value between 0-1.Tanh with sigmoid still like, in fact, tanh is the deformation of sigmoid：tanh(x)
=2sigmoid (2x) -1, unlike sigmoid, tanh is 0 mean value.In recent years, what relu became is becoming increasingly popular.
Its mathematic(al) representation is as follows：F (x)=max (0, x), wherein, input signal<When 0, output is all 0, input signal>0 feelings
Under condition, output is equal to input.The expression formula of maxout functions is as follows：fi(x)=maxj∈[1,k]Zij.Assuming that input node includes x1
And x2, corresponding weight are respectively w1 and w2, further include weight b, then output node Y=f (w1*x1+w2*x2+b), wherein f
For activation primitive.In addition, the number of input layer and output layer is usually one, hidden layer can be made up of multiple layers.
The optimization algorithm includes stochastic gradient descent (Stochastic Gradient Descent, SGD) algorithm, fits
Answering property moments estimation (adaptive moment estimation, adam) algorithm or Momentum algorithms.
Based on the above technical solution, it is described according to the destination call sound characteristic to the call voice of user into
Row adjustment, terminal where the call voice after adjustment is sent to call contact person can include：According to the destination call sound
Sound feature generation adjustment waveform；The call voice waveform of user of the adjustment waveform with obtaining in real time is synthesized, is generated
Call voice data after adjustment；Terminal where call voice data after the adjustment are sent to call contact person.
Wherein, the call voice waveform of user of the adjustment waveform with obtaining in real time being carried out synthesis can include：It will
The adjustment waveform and the call voice waveform of user obtained in real time utilize Pitch synchronous overlap add (Pitch Synchronous
Overlap and Add, PSOLA) method synthesized.
Based on the above technical solution, can also include the following steps：Obtaining rule according to setting, acquisition is single in real time
Position call voice segment.Correspondingly, described synthesize the adjustment waveform with the call voice waveform obtained in real time, generate
Call voice data after adjustment can include：The adjustment waveform is closed with the unit call voice segment waveform
Into the call voice subdata after generation adjustment；Call voice data after the adjustment are sent to where call contact person
Terminal can include：Terminal where call voice subdata after the adjustment is sent to call contact person.
Optionally, it can be to obtain a unit call voice segment every setting duration or often exist that setting, which obtains rule,
Word is obtained during the ending for detecting a word as unit call voice segment, when specifically the dead time can will reach setting
Between when think to detect the ending of a word.
Call voice control method provided in this embodiment generates default feedback model by using nerve network system,
When mobile terminal is under call mode, the contact type of current talking contact person is obtained, contact type is input to
In default feedback model, the destination call sound characteristic for being suitble to the current mobile terminal user of current talking contact person, root are obtained
The call voice of user is adjusted according to destination call sound characteristic, the call voice after adjustment is sent to current talking connection
Terminal where being people realizes and carries out in due course adjustment according to call contact person type to user's communication voice, and no matter user sends out
Which type of the call voice gone out is, the user's communication voice of terminal where being sent to call contact person is all call sound characteristic
What voice matched with current talking contact person, also improve the interest of voice communication.
Fig. 3 is a kind of structure diagram of call voice control device provided by the embodiments of the present application, which can be by soft
Part and/or hardware realization integrate in the terminal.As shown in figure 3, the device includes contact type acquisition module 31, pre-
If feedback model acquisition module 32, destination call sound characteristic acquisition module 33 and call voice adjustment module 34.
The contact type acquisition module 31, for when detecting that current mobile terminal is in call mode, obtaining
The contact type of current talking contact person；
The default feedback model acquisition module 32, for obtaining the default feedback mould generated based on machine learning method
Type, the default feedback model are obtained by the call-information sample training of multiple known users call sound characteristic information, are used for
User's communication sound characteristic based on call contact person type feedback dialogue call contact person；
The destination call sound characteristic acquisition module 33, for the contact type to be input to the default feedback
In model, the destination call sound characteristic of the default feedback model output is obtained；
The call voice adjusts module 34, for according to the destination call sound characteristic information to current mobile terminal
The call voice of user is adjusted, terminal where the call voice after adjustment is sent to current talking contact person.
Device provided in this embodiment realizes and in due course tune is carried out according to call contact person type to user's communication voice
Whole, no matter which type of the call voice that user sends out is, the user's communication voice of terminal where being sent to call contact person is all
Call sound characteristic voice matches with current talking contact person, also improves the interest of voice communication.
Optionally, the call sound characteristic is included in tone color, tone, loudness, the tone, word speed and tongue at least
One.
Optionally, the contact type includes colleague, leader, parent, relative, friend, client, lover or promotes people
Member.
Optionally, described device further includes：
Sample acquisition module, for locally obtaining the history call-information of mobile terminal user from mobile terminal or from pre-
If the history call-information of target user group is obtained in server, as history call-information sample；
Default feedback model generation module, for being instructed using neural network method to the history call-information sample
Practice, generate default feedback model.
Optionally, the neural network method includes input layer, hidden layer and output layer；The default feedback model generation
Module is specifically used for：
The contact type of each call contact person in the history call-information is input to the input layer, and pass through
The calculating of activation primitive corresponding with each node of the hidden layer, output intermediate user call sound characteristic；
Using talking with each call contact person in the intermediate user call sound characteristic and the history call-information
Difference and optimization algorithm between user's communication sound characteristic correct the weight in the activation primitive repeatedly, directly
To the difference between intermediate user call sound characteristic and the user's communication sound characteristic within preset range, obtain
The activation primitive for each node that training is completed generates default feedback model.
Optionally, the call voice adjustment module includes：
Waveform generation unit is adjusted, for generating adjustment waveform according to the destination call sound characteristic；
Call voice data generating unit, for by the adjustment waveform and the call voice waveform of user that in real time obtains
It is synthesized, the call voice data after generation adjustment；
Call voice data transmission unit, for the call voice data after the adjustment to be sent to call contact person institute
In terminal.
Optionally, described device further includes：
Unit call voice segment acquiring unit obtains unit call voice piece in real time for obtaining rule according to setting
Section；
The call voice data generating unit is specifically used for：By the adjustment waveform and the unit call voice segment
Waveform is synthesized, the call voice subdata after generation adjustment；
The call voice data transmission unit is specifically used for：Call voice subdata after the adjustment is sent to logical
Terminal where talking about contact person.
The embodiment of the present application also provides a kind of storage medium for including computer executable instructions, and the computer can perform
When being performed by computer processor for performing a kind of call voice control method, this method includes for instruction：
When detecting that current mobile terminal is in call mode, the contact type of current talking contact person is obtained；
The default feedback model generated based on machine learning method is obtained, the default feedback model is by multiple known users
The call-information sample training of call sound characteristic obtains, for based on call contact person type feedback dialogue call contact person
User's communication sound characteristic；
The contact type is input in the default feedback model, obtains the mesh of the default feedback model output
Mark call sound characteristic；
The call voice of current mobile terminal user is adjusted according to the destination call sound characteristic, after adjustment
Call voice be sent to terminal where the current talking contact person.
Storage medium --- any various types of memory devices or storage device.Term " storage medium " is intended to wrap
It includes：Install medium, such as CD-ROM, floppy disk or magnetic tape equipment；Computer system memory or random access memory, such as
DRAM, DDR RAM, SRAM, EDO RAM, blue Bath (Rambus) RAM etc.；Nonvolatile memory, such as flash memory, magnetic medium
(such as hard disk or optical storage)；Memory component of register or other similar types etc..Storage medium can further include other
The memory or combination of type.In addition, storage medium can be located at program in the first computer system being wherein performed,
Or can be located in different second computer systems, second computer system is connected to the by network (such as internet)
One computer system.Second computer system can provide program instruction and be used to perform to the first computer." storage is situated between term
Matter " can include may reside in different location two of (such as in different computer systems by network connection) or
More storage mediums.Storage medium can store the program instruction that can be performed by one or more processors and (such as implement
For computer program).
Certainly, a kind of storage medium for including computer executable instructions that the embodiment of the present application is provided, computer
The call voice control operation that executable instruction is not limited to the described above, can also be performed what the application any embodiment was provided
Relevant operation in call voice control method.
The embodiment of the present application provides a kind of mobile terminal, can be integrated in the mobile terminal provided by the embodiments of the present application logical
Talk about phonetic controller.Fig. 4 is a kind of structure diagram of mobile terminal provided by the embodiments of the present application.Mobile terminal 400 can
To include：Memory 401, processor 402 and is stored in the computer program that can be run on memory 401 and in processor 402,
The processor 402 realizes the call voice control method as described in the embodiment of the present application when performing the computer program.
Mobile terminal provided by the embodiments of the present application realizes and user's communication voice is carried out according to call contact person type
In due course adjustment, no matter which type of the call voice that user sends out is, the user of terminal is led to where being sent to call contact person
Language sound sound characteristic voice of all conversing matches with current talking contact person, also improves the interest of voice communication.
Fig. 5 is the structure diagram of another mobile terminal provided by the embodiments of the present application, as shown in figure 5, the movement is whole
End can include：Memory 501, central processing unit (Central Processing Unit, CPU) 502 (also known as processor, with
Lower abbreviation CPU), the memory 501, for storing executable program code；The processor 502 is by reading the storage
The executable program code stored in device 501 runs program corresponding with the executable program code, for performing：
When detecting that current mobile terminal is in call mode, the contact type of current talking contact person is obtained；It obtains based on machine
The default feedback model of learning method generation, the default feedback model are believed by the call of multiple known users call sound characteristic
Breath sample training obtains, for the user's communication sound characteristic based on call contact person type feedback dialogue call contact person；It will
The contact type is input in the default feedback model, obtains the destination call sound of the default feedback model output
Feature；The call voice of current mobile terminal user is adjusted according to the destination call sound characteristic, after adjustment
Call voice is sent to terminal where the current talking contact person.
The mobile terminal further includes：Peripheral Interface 503, RF (Radio Frequency, radio frequency) circuit 505, audio-frequency electric
Road 506, loud speaker 511, power management chip 508, input/output (I/O) subsystem 509, touch screen 512, other input/controls
Control equipment 510 and outside port 504, these components are communicated by one or more communication bus or signal wire 507.
It should be understood that diagram mobile terminal 500 is only an example of mobile terminal, and mobile terminal 500
Can have than more or less components shown in figure, two or more components can be combined or can be with
It is configured with different components.Various parts shown in figure can be including one or more signal processings and/or special
Hardware, software including integrated circuit are realized in the combination of hardware and software.
Just provided in this embodiment for the mobile terminal of call voice to be controlled to be described in detail below, the movement is whole
End is by taking smart mobile phone as an example.
Memory 501, the memory 501 can be by access such as CPU502, Peripheral Interfaces 503, and the memory 501 can
To include high-speed random access memory, nonvolatile memory can also be included, such as one or more disk memory,
Flush memory device or other volatile solid-state parts.
The peripheral hardware that outputs and inputs of equipment can be connected to CPU502 and deposited by Peripheral Interface 503, the Peripheral Interface 503
Reservoir 501.
I/O subsystems 509, the I/O subsystems 509 can be by the input/output peripherals in equipment, such as touch screen 512
With other input/control devicess 510, it is connected to Peripheral Interface 503.I/O subsystems 509 can include 5091 He of display controller
For controlling one or more input controllers 5092 of other input/control devicess 510.Wherein, one or more input controls
Device 5092 processed receives electric signal from other input/control devicess 510 or sends electric signal to other input/control devicess 510,
Other input/control devicess 510 can include physical button (pressing button, rocker buttons etc.), dial, slide switch, behaviour
Vertical pole clicks idler wheel.What deserves to be explained is input controller 5092 can with it is following any one connect：Keyboard, infrared port,
The indicating equipment of USB interface and such as mouse.
Touch screen 512, the touch screen 512 are the input interface and output interface between user terminal and user, can
User is shown to depending on output, visual output can include figure, text, icon, video etc..
Display controller 5091 in I/O subsystems 509 receives electric signal from touch screen 512 or is sent out to touch screen 512
Electric signals.Touch screen 512 detects the contact on touch screen, and the contact detected is converted to and shown by display controller 5091
The interaction of user interface object on touch screen 512, that is, realize human-computer interaction, the user interface being shown on touch screen 512
Icon that object can be the icon of running game, be networked to corresponding network etc..What deserves to be explained is equipment can also include light
Mouse, light mouse are the extensions for not showing the touch sensitive surface visually exported or the touch sensitive surface formed by touch screen.
RF circuits 505 are mainly used for establishing the communication of mobile phone and wireless network (i.e. network side), realize mobile phone and wireless network
The data receiver of network and transmission.Such as transmitting-receiving short message, Email etc..Specifically, RF circuits 505 receive and send RF letters
Number, RF signals are also referred to as electromagnetic signal, and RF circuits 505 convert electrical signals to electromagnetic signal or electromagnetic signal is converted to telecommunications
Number, and communicated by the electromagnetic signal with communication network and other equipment.RF circuits 505 can include performing
The known circuit of these functions includes but not limited to antenna system, RF transceivers, one or more amplifiers, tuner, one
A or multiple oscillators, digital signal processor, CODEC (COder-DECoder, coder) chipset, user identifier mould
Block (Subscriber Identity Module, SIM) etc..
Voicefrequency circuit 506 is mainly used for receiving audio data from Peripheral Interface 503, which is converted to telecommunications
Number, and the electric signal is sent to loud speaker 511.
Loud speaker 511 for the voice signal for receiving mobile phone from wireless network by RF circuits 505, is reduced to sound
And play the sound to user.
Power management chip 508, the hardware for being connected by CPU502, I/O subsystem and Peripheral Interface 503 are supplied
Electricity and power management.
It is arbitrary that call voice control device, storage medium and the mobile terminal provided in above-described embodiment can perform the application
The call voice control method that embodiment is provided has and performs the corresponding function module of this method and advantageous effect.Not upper
The technical detail of detailed description in embodiment is stated, reference can be made to the call voice control method that the application any embodiment is provided.
The embodiment of the present application also provides a kind of call voice control device, and described device is integrated in predetermined server, institute
It states device and includes sample acquisition module and default feedback model generation module.
The sample acquisition module, for from mobile terminal obtain mobile terminal user history call-information or from
The history call-information of target user group is obtained in predetermined server local, as history call-information sample；
The default feedback model generation module, for using neural network method to the history call-information sample into
Row training generates default feedback model.
The embodiment of the present application also provides a kind of server, and the server is integrated with above-mentioned including sample acquisition module and pre-
If the call voice control device of feedback model generation module.
The technical principle that above are only the preferred embodiment of the application and used.The application is not limited to spy described here
Determine embodiment, the various significant changes that can carry out for a person skilled in the art, readjust and substitute all without departing from
The protection domain of the application.Therefore, although being described in further detail by above example to the application, this Shen
Above example please be not limited only to, in the case where not departing from the application design, other more equivalence enforcements can also be included
Example, and scope of the present application is determined by the scope of the claims.
Claims (10)
1. a kind of call voice control method, which is characterized in that including：
When detecting that current mobile terminal is in call mode, the contact type of current talking contact person is obtained；
The default feedback model generated based on machine learning method is obtained, the default feedback model is conversed by multiple known users
The call-information sample training of sound characteristic obtains, for the user based on call contact person type feedback dialogue call contact person
Call sound characteristic；
The contact type is input in the default feedback model, the target for obtaining the default feedback model output is led to
Talk about sound characteristic；
The call voice of current mobile terminal user is adjusted according to the destination call sound characteristic, it will be logical after adjustment
Language sound is sent to terminal where the current talking contact person.
2. call voice control method according to claim 1, which is characterized in that the call sound characteristic includes sound
At least one of in color, tone, loudness, the tone, word speed and tongue.
3. according to the method described in claim 1, it is characterized in that, the contact type includes colleague, leader, parent, parent
Relative, friend, client, lover or sales force.
4. call voice control method according to claim 1, which is characterized in that further include：
The history call-information of mobile terminal user, which is locally obtained, from mobile terminal or target is obtained from predetermined server uses
The history call-information of family group, as history call-information sample；
The history call-information sample is trained using neural network method, generates default feedback model.
5. call voice control method according to claim 4, which is characterized in that the neural network method includes input
Layer, hidden layer and output layer；
It is described that the history call-information sample is trained using neural network method, it generates default feedback model and includes：
The contact type of each call contact person in the history call-information is input to the input layer, and pass through and institute
State the calculating of the corresponding activation primitive of each node of hidden layer, output intermediate user call sound characteristic；
Utilize the user for talking with each call contact person in intermediate user call sound characteristic and the history call-information
Difference and optimization algorithm between call sound characteristic correct the weight in the activation primitive repeatedly, until institute
The difference between intermediate user call sound characteristic and the user's communication sound characteristic is stated within preset range, is trained
The activation primitive for each node completed generates default feedback model.
6. call voice control method according to claim 1, which is characterized in that described according to the destination call sound
Feature is adjusted the call voice of user, and terminal where the call voice after adjustment is sent to call contact person includes：
Adjustment waveform is generated according to the destination call sound characteristic；
The call voice waveform of user of the adjustment waveform with obtaining in real time is synthesized, the call voice after generation adjustment
Data；
Terminal where call voice data after the adjustment are sent to call contact person.
7. call voice control method according to claim 6, which is characterized in that further include：Rule is obtained according to setting
Unit call voice segment is obtained in real time；
It is described to synthesize the adjustment waveform with the call voice waveform obtained in real time, the call voice number after generation adjustment
According to including：The adjustment waveform is synthesized with the unit call voice segment waveform, the call voice after generation adjustment
Subdata；
Terminal where call voice data after the adjustment are sent to call contact person includes：By the call after the adjustment
Voice subdata is sent to terminal where call contact person.
8. a kind of call voice control device, which is characterized in that including：
Contact type acquisition module, for when detecting that current mobile terminal is in call mode, obtaining current talking connection
It is the contact type of people；
Default feedback model acquisition module, it is described default for obtaining the default feedback model generated based on machine learning method
Feedback model is obtained by the call-information sample training of multiple known users call sound characteristic information, for being based on call contact
People's type feedback talks with the user's communication sound characteristic of call contact person；
Destination call sound characteristic acquisition module for the contact type to be input in the default feedback model, obtains
Take the destination call sound characteristic of the default feedback model output；
Call voice adjusts module, for according to call of the destination call sound characteristic information to current mobile terminal user
Voice is adjusted, terminal where the call voice after adjustment is sent to current talking contact person.
9. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the program is held by processor
The call voice control method as described in any in claim 1-7 is realized during row.
10. a kind of mobile terminal including memory, processor and stores the calculating that can be run on a memory and on a processor
Machine program, which is characterized in that the processor is realized when performing the computer program as described in any in claim 1-7
Call voice control method.
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