TW201719452A - A system and method for training robots through voice - Google Patents

Abstract

The invention discloses a system and method for training robots through voice, a receiving unit of the system for training robots through voice is used for receiving voice signals; a parsing unit connects to the receiving unit, and is used for parsing the voice signals, matching the voice signals with preset statements, to acquire conditional statements matching with the preset statements and corresponding to the voice signals and execute statements corresponding to the voice signals; a processing unit connects to the parsing unit, and is used for combining the conditional statements and the execute statements to generate a target entry; a storage unit connects to the processing unit, and is used for storing a preset entry, and training robots according to the preset entry; the processing unit performs weighting calculation according to the target entry, and performs corresponding process according to the weighting results.

Description

System and method for training robot by voice

The present invention relates to the field of robots, and more particularly to a system and method for training a robot by voice.

At present, the method of training robot behavior is limited to the use of programming development to modify the logic of the robot. The developer modifies the program logic of the robot to complete the setting of performing certain actions under certain conditions. This training method is necessary for the underlying development of the robot, but when it enters the upper logic development, it has the defects of low development efficiency and high error rate; this training method is not suitable for ordinary users who do not have the professional skills of programming development, if ordinary users If you want to make a small change to the behavior of the robot, it will take a lot of time to learn.

In summary, the above training method has a narrow application range, low efficiency, and high error rate.

In view of the above problems existing in the existing methods of training robots, there is now provided a system and method for implementing support for a robot that supports a robot without speech based on programming.

The specific technical solutions are as follows:

A system for training robots by voice, including:

a receiving unit, configured to receive a voice signal;

An analyzing unit, configured to connect the receiving unit, to parse the voice signal, match the voice signal with a preset statement, and acquire a condition that matches the preset statement and corresponds to the voice signal a statement, and an execution statement corresponding to the voice signal;

a processing unit, coupled to the parsing unit, configured to combine the conditional statement with the execution statement to generate a target entry;

a storage unit, connected to the processing unit, for storing a preset item, and training the robot according to the preset item;

The processing unit performs weight calculation according to the target item, and performs corresponding processing according to the weight calculation result.

Preferably, the parsing unit comprises:

a first conversion module, configured to convert the voice signal into text information;

a semantic analysis module, connected to the first conversion module, configured to parse the text information, match the text information with the preset statement, and obtain a match with the preset statement and The conditional statement corresponding to the text information, and identifying that the conditional statement is a standard conditional statement or a feedback conditional statement;

If the conditional statement is a standard conditional statement, acquiring an execution statement corresponding to the file information;

If the conditional statement is a feedback conditional statement, a weighting operation is performed to cause the robot to perform an operation of the last task.

Preferably, the parsing unit further includes:

A second conversion module is connected to the semantic analysis module for converting the execution statement into a corresponding audio signal and outputting the same.

Preferably, each of the preset items includes a preset condition statement and a preset execution statement.

Preferably, the processing unit traverses the preset conditional statement in all the preset entries in the storage unit according to the conditional statement in the target entry to obtain whether the conditional statement is related to The preset condition statement is repeated, if not repeated, performing the weighting operation, and storing the target item in the storage unit to form a new preset item, and the robot is determined according to the preset item Perform training; if it is repeated, perform the weighting operation, and perform corresponding processing according to the weight calculation result.

A method of training a robot by voice, comprising the following steps:

S1. Acquiring a voice signal;

S2. Parsing the voice signal, matching the voice signal with a preset statement, acquiring a conditional statement matching the preset statement and corresponding to the voice signal, and corresponding to the voice signal Execute statement

S3. Combining the conditional statement with the execution statement to generate a target entry;

S4. Perform weight calculation according to the target item, and perform corresponding processing according to the weight calculation result.

Preferably, the step S2 specifically includes:

S21. converting the voice signal into text information;

S22. Parsing the text information, matching the text information with the preset statement, acquiring a conditional statement matching the preset statement and corresponding to the text information, and identifying the conditional statement Is a standard conditional statement or a feedback conditional statement;

If the conditional statement is a standard conditional statement, acquiring an execution statement corresponding to the file information;

If the conditional statement is a feedback conditional statement, a weighting operation is performed to cause the robot to perform an operation of the last task.

Preferably, the step S2 further includes:

S23. Convert the execution statement into a corresponding audio signal and output.

Preferably, each of the preset items includes a preset condition statement and a preset execution statement.

Preferably, the step S3 specifically includes:

S31. traversing the preset conditional statement in all the preset entries in the storage unit according to the conditional statement in the target entry;

S32. Acquire a traversal result, and determine whether the conditional statement is repeated with the preset conditional statement.

If the conditional statement does not overlap with the preset conditional statement, step S33 is performed;

If the conditional statement is repeated with the preset conditional statement, step S34 is performed;

S33. Perform the weighting operation, and store the target item in the storage unit to form a new preset item, and train the robot according to the preset item;

S34. Perform the weighting operation, and perform corresponding processing according to the weight calculation result.

The beneficial effects of the above technical solutions:

In the technical solution, in the system for training the robot by voice, the parsing unit parses the speech signal to obtain a corresponding conditional statement and an execution statement, and the processing unit combines the conditional statement and the execution statement to generate an entry, so that the robot according to the entry The corresponding training is carried out with high efficiency and low error rate. In the method of training the robot by voice, the robot can be trained only by inputting a voice signal, and the operation is simple, and the scope of application is wide and the efficiency is high.

The technical solutions in the embodiments of the present invention are clearly and completely described in the following with reference to the accompanying drawings in the embodiments of the present invention. It is obvious that the described embodiments are only a part of the embodiments of the present invention, but not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present invention without creative efforts are within the scope of the present invention.

It should be noted that the embodiments in the present invention and the features in the embodiments may be combined with each other without conflict.

The invention is further illustrated by the following figures and specific examples, but is not to be construed as limiting.

As shown in FIG. 1, a system for training a robot by voice, comprising:

a receiving unit 1 for receiving a voice signal;

An analyzing unit 2 is connected to the receiving unit 1 for parsing the voice signal, matching the voice signal with the preset statement, acquiring a conditional statement matching the preset sentence and corresponding to the voice signal, and corresponding to the voice signal Execute statement

a processing unit 3, connected to the parsing unit 2, for combining a conditional statement with an execution statement to generate a target entry;

a storage unit 4, connected to the processing unit 3, for storing a preset item, and training the robot according to the preset item;

The processing unit 3 performs weight calculation according to the target entry, and performs corresponding processing according to the weight calculation result.

In the present embodiment, the system for training robots by voice can be applied to children's toys. Although children do not have professional programming development skills, children can communicate with robots through natural language and train robots to perform corresponding actions.

In this embodiment, for the optimization process of the robot behavior logic development, a method suitable for the common user to interact with the robot is selected, so that the user concentrates on the training logic itself in the process of training the robot, instead of developing the language, improving the work. Efficiency and reduced error rate. The parsing unit 2 parses the speech signal to obtain a corresponding conditional statement and an execution statement, and the processing unit 3 combines the conditional statement and the execution statement to generate an item, so that the robot performs corresponding training according to the item, and the efficiency is high and the error rate is low.

In a preferred embodiment, the parsing unit 2 comprises:

a first conversion module 21 for converting a voice signal into text information;

a semantic analysis module 22 is connected to the first conversion module 21 for parsing the text information, matching the text information with the preset statement, and acquiring a conditional statement matching the preset sentence and corresponding to the text information, and The recognition conditional statement is a standard conditional statement or a feedback conditional statement;

If the conditional statement is a standard conditional statement, obtaining an execution statement corresponding to the file information;

If the conditional statement is a feedback conditional statement, a weighting operation is performed to cause the robot to perform the operation of the previous task.

In this embodiment, the sentence corresponding to the target entry may be: when A, B; if A, then B; do not do B when A; B should be done at this time; this is wrong; It is wrong to do it.

Among them, "when A", "if A", "don't be at A", and "this time" are standard conditional statements; "this is wrong" and "this is wrong" Feedback conditional statement.

The whole training process of the system for training the robot by voice is: when the training key sentence pattern is recognized, the robot enters the training mode, and the user can use the semantic analysis module of the parsing unit 2 when using the sentence pattern similar to the above to talk with the robot. 22 The user's words are divided into part A and part B. After semantic conversion, part A is converted into a conditional development statement, part B is converted into an execution action development statement, and the relationship between part A and part B is added to the local training. Knowledge base (storage unit 4), and combines part A and part B to form a new entry. If part A is the same as the conditional development statement in the training knowledge base, part B is different from the corresponding execution action development statement in the training knowledge base. Then part A is a knowledge item with the same two conditions but performing different actions, and a weight operation is required. The weight operation includes the user's positive and negative feedback, the additional time is considered, and the new knowledge item is added to the local knowledge base, and the training knowledge base is updated. . When the normal natural language communication is recognized, the training mode ends, the robot ends the training and returns to the polling judgment mode, and all the entries in the knowledge base are trained, and when a certain knowledge item is hit, the execution included in the knowledge item is executed. Action development statement.

In this embodiment, the first conversion module 21 can adopt Automatic Speech Recognition (ASR) technology, and the ASR technology can convert the vocabulary content in the human voice into computer readable content and input the computer into the computer, and the computer Interact.

The semantic analysis module 22 uses artificial intelligence natural language processing (NLP) technology to obtain conditional statements and execution statements in the text information through the NLP technology.

In a preferred embodiment, the parsing unit 2 further includes:

A second conversion module 23 is connected to the semantic analysis module 22 for converting the execution statement into a corresponding audio signal and outputting it.

In this embodiment, the second conversion module 23 uses TTS (Text To Speech) to convert the text into a voice technology, which is part of the human-machine dialogue, and enables the robot to speak through the TTS.

In a preferred embodiment, each of the preset entries includes a preset conditional statement and a preset execution statement.

In a preferred embodiment, the processing unit 3 traverses the preset conditional statements in all the preset entries in the storage unit according to the conditional statement in the target entry to obtain whether the conditional statement is repeated with the preset conditional statement, if not Then, the weighting operation is performed, and the target item is stored in the storage unit 4 to form a new preset item, and the robot is trained according to the preset item; if it is repeated, the weighting operation is performed, and the corresponding processing is performed according to the weight calculation result.

In this embodiment, after adding a new knowledge item or an original knowledge item, when receiving the positive and negative feedback of the user, performing weight calculation, arranging the entire training knowledge base, performing compression, etc., to ensure that the robot is in conditional polling. The efficiency at the time of judgment.

As shown in FIG. 2, a method for training a robot by voice includes the following steps:

S1. Acquiring a voice signal;

S2. parsing the voice signal, matching the voice signal with the preset statement, acquiring a conditional statement matching the preset statement and corresponding to the voice signal, and an execution statement corresponding to the voice signal;

S3. Combining the conditional statement with the execution statement to generate a target entry;

S4. Perform weight calculation according to the target item, and perform corresponding processing according to the weight calculation result.

In this embodiment, the robot can be trained only by inputting a voice signal, and the operation is simple, the scope of application is wide, and the efficiency is high.

As shown in FIG. 3, in a preferred embodiment, step S2 specifically includes:

S21. Converting the voice signal into text information;

S22. Parsing the text information, matching the text information with the preset statement, obtaining a conditional statement matching the preset statement and corresponding to the text information, and identifying that the conditional statement is a standard conditional statement or a feedback conditional statement;

If the conditional statement is a standard conditional statement, obtaining an execution statement corresponding to the file information;

If the conditional statement is a feedback conditional statement, a weighting operation is performed to cause the robot to perform the operation of the previous task.

In this embodiment, the voice signal is converted into text information by using Automatic Speech Recognition (ASR) technology, which converts vocabulary content in human speech into computer readable input and interacts with a computer. .

The text information can be parsed by the artificial language natural language processing (NLP) technology, and the conditional statements and execution statements in the text information are obtained by the NLP technology.

In a preferred embodiment, step S2 further includes:

S23. Convert the execution statement to the corresponding audio signal and output it.

In this embodiment, the TTS (Text To Speech) technique is used to convert the execution statement into a corresponding audio signal, which is part of the human-machine dialogue, enabling the robot to speak through the TTS.

In a preferred embodiment, each of the preset entries includes a preset conditional statement and a preset execution statement.

As shown in FIG. 4, in a preferred embodiment, step S3 specifically includes:

S31. traversing a preset conditional statement in all preset entries in the storage unit according to the conditional statement in the target entry;

S32. Acquire traversal results and perform weight calculation

Determine whether the conditional statement is a duplicate of a preset conditional statement.

If the conditional statement does not overlap with the preset conditional statement, step S33 is performed;

If the conditional statement is repeated with the preset conditional statement, step S34 is performed;

S33. Perform a weighting operation, and store the target item in the storage unit to form a new preset item, and train the robot according to the preset item;

S34. Perform a weighting operation and perform corresponding processing according to the weight calculation result.

In this embodiment, when the robot hears the user saying "hello" in the afternoon, the training steps of the user training robot to reply "XXX (person name), good afternoon" are as follows: A1. The user says "hello" to the robot, "This time should say, XXX, good afternoon" A2. Semantic analysis of the content spoken by the user, separating the execution statement in the speech content, that is, "say XXX, good afternoon", "say" is the TTS service corresponding to the robot, " XXX" hits the name of the currently interacting user, "afternoon" hits the current time, "XXX, good afternoon" corresponds to the content of the TTS service; A3. Generates a new knowledge base entry based on the semantic analysis result, and then adds the weight to the local knowledge after determining the weight A4. The robot executes a new knowledge base and ends; after completing this interactive training, when the user says "hello" to the robot, the robot will answer "XXX, Good afternoon" to achieve the intended training purpose.

The invention enables the user to free hands when training the robot, and realizes the modification of the robot behavior without writing any code, so that the user concentrates more on the training content itself in the training process, instead of how to write the code and the like. The problem.

The above is only a preferred embodiment of the present invention, and is not intended to limit the scope of the embodiments and the scope of the present invention, and those skilled in the art should be able to Alternatives and obvious variations are intended to be included within the scope of the invention.

1‧‧‧ receiving unit
2‧‧‧ analytical unit
21‧‧‧First conversion module
22‧‧‧Semantic Analysis Module
23‧‧‧Second conversion module
3‧‧‧Processing unit
4‧‧‧ storage unit
S1-S4‧‧‧ steps
S21-S23‧‧‧Steps
S31-S34‧‧‧Steps

1 is a block diagram of an embodiment of a system for training a robot by voice according to the present invention; FIG. 2 is a flow chart of an implementation of a method for training a robot by voice according to the present invention; A flowchart of a method for parsing a speech signal; FIG. 4 is a flowchart of a method for performing corresponding processing on the target entry according to a traversal result.

1‧‧‧ receiving unit

2‧‧‧ analytical unit

21‧‧‧First conversion module

22‧‧‧Semantic Analysis Module

23‧‧‧Second conversion module

3‧‧‧Processing unit

4‧‧‧ storage unit

Claims (10)

A system for training a robot by voice, comprising: a receiving unit for receiving a voice signal; a parsing unit connected to the receiving unit, configured to parse the voice signal, and the voice signal and a preset The statement is matched to obtain a conditional statement matching the preset statement and corresponding to the voice signal, and an execution statement corresponding to the voice signal; a processing unit, connected to the parsing unit, for The conditional statement is combined with the execution statement to generate a target entry; a storage unit is connected to the processing unit for storing a preset entry, and the robot is trained according to the preset entry; the processing unit is configured according to the target entry The weight calculation is performed, and corresponding processing is performed according to the weight calculation result. The system for training a robot by voice according to the first aspect of the invention, wherein the parsing unit comprises: a first conversion module, configured to convert the voice signal into text information; a semantic analysis module, The first conversion module is connected to parse the text information, and the text information is matched with the preset statement to obtain a matching with the preset statement and corresponding to the text information. a conditional statement, and identifying that the conditional statement is a standard conditional statement or a feedback conditional statement; if the conditional statement is a standard conditional statement, acquiring an execution statement corresponding to the file information; if the conditional statement is feedback For the conditional statement, a weighting operation is performed to cause the robot to perform the operation of the previous task. The system for training a robot by voice according to the second aspect of the patent application, the parsing unit further comprising: a second conversion module, connected to the semantic analysis module, configured to convert the execution statement into Corresponding audio signal and output. A system for training a robot by voice according to the second aspect of the patent application, each of the preset items includes a preset conditional statement and a preset execution statement. The system for training a robot by voice according to claim 4, wherein the processing unit traverses all of the preset entries in the storage unit according to the conditional statement in the target entry. The preset conditional statement to obtain whether the conditional statement is repeated with the preset conditional statement, if not, performing the weighting operation, and storing the target item in the storage unit to form The new preset item is trained according to the preset item; if it is repeated, the weighting operation is performed, and corresponding processing is performed according to the weight calculation result. A method for training a robot by voice, comprising the following steps: S1. collecting a voice signal; S2. parsing the voice signal, matching the voice signal with a preset statement, and acquiring the preset statement a conditional statement matching the voice signal and an execution statement corresponding to the voice signal; S3. combining the conditional statement with the execution statement to generate a target entry; S4. performing, according to the target entry The weight is calculated and processed according to the weight calculation result. The method for training a robot by voice according to the sixth aspect of the patent application, the step S2 specifically includes: S21. converting the voice signal into text information; S22. parsing the text information, Matching the text information with the preset statement, acquiring a conditional statement that matches the preset statement and corresponding to the text information, and identifying that the conditional statement is a standard conditional statement or a feedback conditional statement; The conditional statement is a standard conditional statement, and an execution statement corresponding to the file information is acquired; if the conditional statement is a feedback conditional statement, a weighting operation is performed to cause the robot to perform an operation of the previous task. The method for training a robot by voice according to the seventh aspect of the patent application, the step S2 further includes: S23. Converting the execution statement into a corresponding audio signal, and outputting. The method for training a robot by voice according to item 6 of the patent application scope, each of the preset items includes a preset condition statement and a preset execution statement. The method for training a robot by voice according to the ninth application of the patent application, the step S3 specifically includes: S31. traversing all of the storage units according to the conditional statement in the target entry. Determining the preset conditional statement in the entry; S32. Obtaining a traversal result, determining whether the conditional statement is repeated with the preset conditional statement, if the conditional statement does not overlap with the preset conditional statement, Step S33 is performed; if the conditional statement is repeated with the preset conditional statement, step S34 is performed; S33. performing the weighting operation, and storing the target entry in the storage unit to form a new Presetting an item, training the robot according to the preset item; S34. performing the weighting operation, and performing corresponding processing according to the weighting calculation result.