CN112309376A - Telemedicine voice processing method and electronic equipment - Google Patents
Telemedicine voice processing method and electronic equipment Download PDFInfo
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- CN112309376A CN112309376A CN202011179905.1A CN202011179905A CN112309376A CN 112309376 A CN112309376 A CN 112309376A CN 202011179905 A CN202011179905 A CN 202011179905A CN 112309376 A CN112309376 A CN 112309376A
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- 238000000034 method Methods 0.000 claims abstract description 14
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- 125000004122 cyclic group Chemical group 0.000 claims description 48
- 238000013528 artificial neural network Methods 0.000 claims description 22
- 230000000306 recurrent effect Effects 0.000 claims description 20
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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
- G10L15/08—Speech classification or search
- G10L15/16—Speech classification or search using artificial neural networks
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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
- G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
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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
- G10L15/28—Constructional details of speech recognition systems
- G10L15/34—Adaptation of a single recogniser for parallel processing, e.g. by use of multiple processors or cloud computing
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Abstract
The application provides a speech processing method for telemedicine, which comprises the following steps: the electronic equipment processes the telemedicine voice to obtain the meaning of the voice. The technical scheme provided by the application has the advantages of reducing the calculation power consumption and improving the user experience.
Description
Technical Field
The application relates to the fields of voice and medical treatment, in particular to a voice processing method and electronic equipment for remote medical treatment.
Background
In the prior art, artificial intelligence has been applied to many fields, such as speech recognition and the like. Neural networks in artificial intelligence have the largest potential at present, and most researchers put the research and development into the field reversely.
For the neural network, the existing artificial intelligence has large calculation amount and high cost when carrying out voice recognition.
Disclosure of Invention
The invention aims to provide a voice processing method for remote medical treatment, and the technical scheme can reduce the calculation overhead, reduce the power consumption and improve the user experience.
In a first aspect, a method for speech processing in telemedicine is provided, the method being performed by an electronic device comprising: 5G chip and artificial intelligence chip, artificial intelligence chip structure includes: the delta group slave processing circuit comprises a main processing circuit and delta group slave processing circuits, wherein each group of slave processing circuits comprises: the system comprises a plurality of slave processing circuits, 1 broadcast forwarding circuit and 1 multi-way selection switch, wherein the multi-way selection switch is 1P 2T; delta ports of the main processing circuit are respectively connected with each broadcast forwarding circuit of the delta group of slave processing circuits, and each broadcast forwarding circuit is respectively connected with the broadcast ports of a plurality of slave processing circuits of the same group of slave processing circuits;
the other delta ports of the main processing circuit are respectively connected with the P port of each 1P2T of the delta groups of the slave processing circuits, and two T ports of each 1P2T are respectively connected with the adjacent first slave processing circuit and the second slave processing circuit in each group of the slave processing circuits; the slave processing circuit is also connected with other adjacent slave processing circuits in the same group of slave processing circuits through two forwarding ports; the method comprises the following steps:
the electronic equipment acquires a first voice of telemedicine, and the 5G chip extracts input data X of the first voice at the t-1 momentt-1Inputting the data Xt-1Determining the input data as the input data of the recurrent neural network at the t-1 th moment;
the 5G chip inputs data Xt-1Inputting the weight value W into a main processing circuit, calling the weight value W of the recurrent neural network by the main processing circuit, and executing t-1 layer operation to obtain a hidden layer output result S at t-1 momentt-1And t-1 layer output result Ot-1；
The main processing circuit receives input data X of the first voice at the time t extracted by the 5G chipt(ii) a Mixing XtAnd St-1Performing an addition operation to obtain (X)t+St-1) (ii) a Will (X)t+St-1) Determining the data to be cyclic conversion data, determining the weight W to be broadcast forwarding data, cutting the broadcast forwarding data into a plurality of broadcast forwarding data blocks, respectively broadcasting the broadcast forwarding data blocks to a broadcast forwarding circuit through delta ports, cutting the cyclic forwarding data into alpha groups of cyclic forwarding data blocks, and sending the cyclic forwarding data blocks to a first slave processing circuit and a second slave processing circuit through a 1P2T switch;
the broadcast forwarding circuit forwards the received broadcast forwarding data block to a plurality of slave processing circuits in the same group of slave processing circuits; 1P2T connects one T port to send to the first slave processing circuit when receiving a group of circulation forwarding data blocks, and connects another T port to send to the second slave processing circuit when receiving another group of circulation forwarding data blocks;
when the first slave processing circuit receives a group of cyclic forwarding data blocks, intercepting a local cyclic forwarding data block from the group of cyclic forwarding data blocks, and forwarding the rest cyclic forwarding data blocks to other slave processing circuits anticlockwise; when the second slave processing circuit receives another group of cyclic forwarding data blocks, intercepting local cyclic forwarding data blocks from the group of cyclic forwarding data blocks, and forwarding the rest cyclic forwarding data blocks to other slave processing circuits clockwise;
the slave processing circuit receives the residual cyclic forwarding data block through one forwarding port, receives the broadcast forwarding data block through the broadcast port, intercepts the local cyclic forwarding data block from the residual cyclic forwarding data block, and sends other cyclic forwarding data blocks to other adjacent slave processing circuits through another forwarding port; performing inner product operation on the local circulation forwarding data block and the broadcast forwarding data block to obtain an operation result, and sending the operation result to the broadcast forwarding circuit through the broadcast port;
the broadcast forwarding circuit forwards the operation result to the main processing circuit; the main processing circuit obtains the hidden layer output S of the t-th layer according to the operation resulttAnd the tth output result Ot。
Basis of artificial intelligence chip St、OtAnd executing subsequent operation of the t-th layer to obtain a result of the recurrent neural network, and obtaining the meaning of the first voice according to the result of the recurrent neural network.
In a second aspect, an electronic device is provided, which is configured to perform the method provided in the first aspect.
Optionally, the electronic device includes: smart mobile phone, panel computer, VR equipment, smart glasses, smart TV, elevator advertising terminal or smart sound box.
The method provided by the application needs to perform 2 times of matrix product operation when the recurrent neural network performs operation calculation at the t-th layer, and then performs one time of matrix addition operation, and the technical scheme of the application firstly performs addition operation on 2 matrixes to obtain (X)t+St-1) Then, matrix multiplication is performed, so that 1 matrix multiplication operation is reduced,reduce the amount of calculation, increase the calculation energy consumption of the chip, reduce the power, and in addition, will (X)t+St-1) And the weight W is respectively determined as cycle forwarding data and broadcast data and is realized through two ports, so that the forwarding data volume of one port is reduced compared with the situation that broadcasting and cycle forwarding are carried out on one port, compared with the prior art (for example, an H-shaped structure patent of the department of martial arts), the data transmission quantity of the port of the main processing circuit can be reduced, and the forwarding data volume of the conversion circuit is also reduced, in addition, through setting a 1P2T switch and setting clockwise and anticlockwise different cycle forwarding directions, the forwarding data volume and the operation volume of the slave processing circuit can be the same, the data forwarding can be more balanced, the calculation efficiency is improved, and the user experience degree is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic diagram of the connection between a 5G chip and an artificial intelligence chip provided by the present invention.
Fig. 2 is a flow chart of a telemedicine voice processing method provided by the present invention.
Fig. 3 is a schematic diagram of the architecture of the recurrent neural network provided by the present invention.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The embodiments of the present application will be described below with reference to the drawings.
The term "and/or" in this application is only one kind of association relationship describing the associated object, and means that there may be three kinds of relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" in this document indicates that the former and latter related objects are in an "or" relationship.
The "plurality" appearing in the embodiments of the present application means two or more. The descriptions of the first, second, etc. appearing in the embodiments of the present application are only for illustrating and differentiating the objects, and do not represent the order or the particular limitation of the number of the devices in the embodiments of the present application, and do not constitute any limitation to the embodiments of the present application. The term "connect" in the embodiments of the present application refers to various connection manners, such as direct connection or indirect connection, to implement communication between devices, which is not limited in this embodiment of the present application.
In the present application, "|" means an absolute value.
Referring to fig. 1, fig. 1 provides a schematic structural diagram of a chip based on 5G and an artificial intelligence chip, as shown in fig. 1, the artificial intelligence chip structure includes: a master processing circuit 101, δ sets of slave processing circuits, each set of slave processing circuits comprising: a plurality of slave processing circuits 102, 1 broadcast relay circuit 103, and 1 multiplexer (1P 2T);
wherein, δ ports of the main processing circuit 101 are connected with each broadcast forwarding circuit 102 of δ groups of slave processing circuits, and each broadcast forwarding circuit is connected with broadcast ports of a plurality of slave processing circuits 102 of the same group of slave processing circuits;
the other delta ports of the main processing circuit 101 are connected with the P port of each multi-way selector switch 1P2T of each delta group of slave processing circuits, and two T ports of each multi-way selector switch 1P2T are respectively connected with the adjacent first slave processing circuit and the second slave processing circuit in each group of slave processing circuits; the slave processing circuit 102 is also connected to other adjacent slave processing circuits within the same group of slave processing circuits through two forwarding ports.
Referring to fig. 2, fig. 2 further provides a telemedicine speech processing method, where the method is performed by an electronic device, the electronic device may include a 5G chip and an artificial intelligence chip structure as shown in fig. 1, and the method is shown in fig. 2, and includes the following steps:
step S201, the electronic equipment acquires a first voice of telemedicine, and the 5G chip extracts input data X of the first voice at t-1 momentt-1Inputting the data Xt-1Determining the input data as the input data of the recurrent neural network at the t-1 th moment;
step S202, 5G chip inputs data Xt-1Inputting the weight value W into a main processing circuit, calling the weight value W of the recurrent neural network by the main processing circuit, and executing t-1 layer operation to obtain a hidden layer output result S at t-1 momentt-1And t-1 layer output result Ot-1；
Step S203, the main processing circuit receives the input data X of the first voice at the time t extracted by the 5G chipt(ii) a Mixing XtAnd St-1Performing an addition operation to obtain (X)t+St-1) (ii) a Will (X)t+St-1) Determining the data to be cyclic conversion data, determining the weight W to be broadcast forwarding data, cutting the broadcast forwarding data into a plurality of broadcast forwarding data blocks, respectively broadcasting the broadcast forwarding data blocks to a broadcast forwarding circuit through delta ports, cutting the cyclic forwarding data into alpha groups of cyclic forwarding data blocks, and sending the cyclic forwarding data blocks to a first slave processing circuit and a second slave processing circuit through a 1P2T switch;
step S204, the broadcast forwarding circuit forwards the received broadcast forwarding data block to a plurality of slave processing circuits in the same group of slave processing circuits; 1P2T connects one T port to send to the first slave processing circuit when receiving a group of circulation forwarding data blocks, and connects another T port to send to the second slave processing circuit when receiving another group of circulation forwarding data blocks;
step S205, when the first slave processing circuit receives a group of cyclic forwarding data blocks, intercepting a local cyclic forwarding data block from the group of cyclic forwarding data blocks, and forwarding the remaining cyclic forwarding data blocks to other slave processing circuits anticlockwise; when the second slave processing circuit receives another group of cyclic forwarding data blocks, intercepting local cyclic forwarding data blocks from the group of cyclic forwarding data blocks, and forwarding the rest cyclic forwarding data blocks to other slave processing circuits clockwise;
step S206, the slave processing circuit receives the residual cyclic forwarding data block through one forwarding port, receives the broadcast forwarding data block through the broadcast port, intercepts the local cyclic forwarding data block from the residual cyclic forwarding data block, and sends other cyclic forwarding data blocks to other adjacent slave processing circuits through another forwarding port; executing inner product operation (multiply-add operation) on the local circulation forwarding data block and the broadcast forwarding data block to obtain an operation result, and sending the operation result to the broadcast forwarding circuit through the broadcast port;
step S207, the broadcast forwarding circuit forwards the operation result to the main processing circuit; the main processing circuit obtains the hidden layer output S of the t-th layer according to the operation resulttAnd the tth output result Ot。
Step S208, the artificial intelligence chip is based on St、OtAnd executing subsequent operation of the t-th layer to obtain a result of the recurrent neural network, and obtaining the meaning of the first voice according to the result of the recurrent neural network.
The artificial intelligence chip is based on St、OtThe subsequent operation of the t-th layer can be referred to the operation of the t-th layer to obtain the output result of the corresponding layer, and the meaning of the first voice obtained according to the output result can be confirmed by adopting the existing recurrent neural network, such as a recurrent neural network operation system of google.
The technical scheme provided by the application can reduce the calculation amount of the recurrent neural network, the recurrent neural network is a neural network model commonly used for speech translation, and the structure of the recurrent neural network is shown in figure 3 and comprises an input layer, a hidden layer and an output layer, wherein the output structure of the hidden layer is used as input data of the hidden layer at the next moment.
As shown in fig. 3, the output result of the hidden layer at time t is the output of the hidden layer at the next time t +1, for example.
As shown in FIG. 3, where W represents the weight, Xt-1Input data of the input layer representing the time t-1, XtInput data of the input layer representing time t, St-1Output result of hidden layer representing time t-1, Ot-1The output result of the output layer at the time t-1 is shown;
taking time t as an example:
St＝Xt×W+St-1×W
Ot＝f(St)
where f represents an activation function including, but not limited to: sigmoid function, tanh function, etc.
Of course, in practical applications, other activation functions may be used.
As shown in fig. 3, when the recurrent neural network performs operation calculation at the t-th layer, it needs to perform matrix multiplication operation 2 times, and then perform matrix addition operation one time, whereas the technical solution of the present application performs addition operation on 2 matrices to obtain (X)t+St-1) Then carrying out matrix multiplication operation so as to reduce 1 time of matrix multiplication operation, reduce calculation quantity, raise calculation energy consumption of chip and reduce power, in addition, it can reduce (X)t+St-1) And the weight W is respectively determined as cycle forwarding data and broadcast data and is realized through two ports, thus compared with the broadcast and cycle forwarding at one port, the weight W is reducedThe forwarding data volume of a port is reduced, compared with the prior art (for example, the H-shaped structure patent of the middle Council and the martial arts), the data transmission quantity of the ports of the main processing circuit can be reduced, the forwarding data volume of the conversion circuit is also reduced, in addition, the 1P2T switch is arranged, the clockwise and anticlockwise different circulation forwarding directions are arranged, the forwarding data volume and the operation amount of the slave processing circuit are the same, the data forwarding can be more balanced, the calculation efficiency is improved, and the user experience is improved.
In an alternative, the δ sets of slave processing circuits are 6 sets of slave processing circuits, and each set of slave processing circuits is 6 slave processing circuits.
The intercepting of the local loop forwarding data block from the group of loop forwarding data blocks may specifically include: a row of element values or a column of element values is intercepted from a group of loop forwarding data blocks to determine the local loop forwarding data blocks. Can be (X)t+St-1) A row of element values or a column of element values.
The embodiment of the application also provides electronic equipment, and the electronic equipment is used for executing the method.
The electronic device includes: smart phones, tablet computers, VR devices, smart glasses, smart televisions, or smart speakers.
The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the method and the core concept of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (4)
1. A telemedicine speech processing method, the method being performed by an electronic device comprising: 5G chip and artificial intelligence chip, artificial intelligence chip structure includes: the delta group slave processing circuit comprises a main processing circuit and delta group slave processing circuits, wherein each group of slave processing circuits comprises: the system comprises a plurality of slave processing circuits, 1 broadcast forwarding circuit and 1 multi-way selection switch, wherein the multi-way selection switch is 1P 2T; delta ports of the main processing circuit are respectively connected with each broadcast forwarding circuit of the delta group of slave processing circuits, and each broadcast forwarding circuit is respectively connected with the broadcast ports of a plurality of slave processing circuits of the same group of slave processing circuits;
the other delta ports of the main processing circuit are respectively connected with the P port of each 1P2T of the delta groups of the slave processing circuits, and two T ports of each 1P2T are respectively connected with the adjacent first slave processing circuit and the second slave processing circuit in each group of the slave processing circuits; the slave processing circuit is also connected with other adjacent slave processing circuits in the same group of slave processing circuits through two forwarding ports; the method comprises the following steps:
the electronic equipment acquires a first voice of telemedicine, and the 5G chip extracts input data X of the first voice at the t-1 momentt-1Inputting the data Xt-1Determining the input data as the input data of the recurrent neural network at the t-1 th moment;
the 5G chip inputs data Xt-1Inputting the weight value W into a main processing circuit, calling the weight value W of the recurrent neural network by the main processing circuit, and executing t-1 layer operation to obtain a hidden layer output result S at t-1 momentt-1And t-1 layer output result Ot-1；
The main processing circuit receives input data X of the first voice at the time t extracted by the 5G chipt(ii) a Mixing XtAnd St-1Performing an addition operation to obtain (X)t+St-1) (ii) a Will (X)t+St-1) Determining the data to be cyclic conversion data, determining the weight W to be broadcast forwarding data, cutting the broadcast forwarding data into a plurality of broadcast forwarding data blocks, respectively broadcasting the broadcast forwarding data blocks to a broadcast forwarding circuit through delta ports, cutting the cyclic forwarding data into alpha groups of cyclic forwarding data blocks, and sending the cyclic forwarding data blocks to a first slave processing circuit and a second slave processing circuit through a 1P2T switch;
the broadcast forwarding circuit forwards the received broadcast forwarding data block to a plurality of slave processing circuits in the same group of slave processing circuits; 1P2T connects one T port to send to the first slave processing circuit when receiving a group of circulation forwarding data blocks, and connects another T port to send to the second slave processing circuit when receiving another group of circulation forwarding data blocks;
when the first slave processing circuit receives a group of cyclic forwarding data blocks, intercepting a local cyclic forwarding data block from the group of cyclic forwarding data blocks, and forwarding the rest cyclic forwarding data blocks to other slave processing circuits anticlockwise; when the second slave processing circuit receives another group of cyclic forwarding data blocks, intercepting local cyclic forwarding data blocks from the group of cyclic forwarding data blocks, and forwarding the rest cyclic forwarding data blocks to other slave processing circuits clockwise;
the slave processing circuit receives the residual cyclic forwarding data block through one forwarding port, receives the broadcast forwarding data block through the broadcast port, intercepts the local cyclic forwarding data block from the residual cyclic forwarding data block, and sends other cyclic forwarding data blocks to other adjacent slave processing circuits through another forwarding port; performing inner product operation on the local circulation forwarding data block and the broadcast forwarding data block to obtain an operation result, and sending the operation result to the broadcast forwarding circuit through the broadcast port;
the broadcast forwarding circuit forwards the operation result to the main processing circuit; the main processing circuit obtains the hidden layer output S of the t-th layer according to the operation resulttAnd the tth output result Ot。
Basis of artificial intelligence chip St、OtAnd executing subsequent operation of the t-th layer to obtain a result of the recurrent neural network, and obtaining the meaning of the first voice according to the result of the recurrent neural network.
2. The method of claim 1,
each set of 6 slave processing circuits.
3. An electronic device, characterized in that the electronic device is adapted to perform the method of any of claims 1-2.
4. The electronic device of claim 3,
the electronic device includes: smart phones, tablet computers, VR devices, smart glasses, smart televisions, or smart speakers.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011179905.1A CN112309376A (en) | 2020-10-29 | 2020-10-29 | Telemedicine voice processing method and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011179905.1A CN112309376A (en) | 2020-10-29 | 2020-10-29 | Telemedicine voice processing method and electronic equipment |
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| Publication Number | Publication Date |
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| CN112309376A true CN112309376A (en) | 2021-02-02 |
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| Application Number | Title | Priority Date | Filing Date |
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| CN202011179905.1A Withdrawn CN112309376A (en) | 2020-10-29 | 2020-10-29 | Telemedicine voice processing method and electronic equipment |
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| CN (1) | CN112309376A (en) |
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2020
- 2020-10-29 CN CN202011179905.1A patent/CN112309376A/en not_active Withdrawn
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