CN111292741B - Intelligent voice interaction robot - Google Patents
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Abstract
The invention discloses an intelligent voice interaction robot which comprises an intelligent interaction system, wherein the intelligent interaction system comprises a user image module, a database module, a voice acquisition module, a data processing module and an execution module. Decomposing instruction information in a voice interaction word bank and user voice input information into word vectors according to the parts of speech; then, calculating a similarity value between the instruction information and the input information by using a convolutional neural network, and providing corresponding feedback information for a user according to the similarity value; according to the method and the device, fuzzy sound conversion processing can be carried out on the input information which cannot be successfully matched, then the input information which is subjected to the fuzzy sound conversion processing is matched with the instruction information, and the dialect voice information recognition efficiency can be improved.
Description
Technical Field
The invention relates to an intelligent voice interaction robot.
Background
With the rapid development of scientific technology, intelligent robots participate in various industries, bring great convenience to production and life of human beings, and enable people to repeat the process from heavy to heavy. At present, the application of the voice recognition technology in the control of the intelligent robot is continuously expanded, and a voice recognition system of the intelligent robot has higher recognition precision. However, due to the fact that part of users pronounce differently due to dialects, the voice recognition efficiency of the voice recognition system of the intelligent robot is easy to result.
Disclosure of Invention
The invention aims to provide an intelligent voice interaction robot to solve the problem that the existing robot is low in voice recognition efficiency due to dialect pronunciation difference.
In order to solve the above technical problem, the present invention provides an intelligent voice interaction robot, which includes an intelligent interaction system, wherein the intelligent interaction system includes:
the user image module is used for acquiring user registration information and sound information to establish a user image;
the database module is used for constructing a robot voice interaction word bank according to the general robot instruction information and matching corresponding feedback information for each instruction information in the voice interaction word bank; performing word segmentation processing on each piece of instruction information in the voice interaction word bank according to a part-of-speech classification standard to obtain a plurality of reference word vectors;
the voice acquisition module is used for acquiring voice information of a registered user and preprocessing the acquired voice information to obtain standardized voice input information;
the data processing module is used for carrying out word segmentation processing on the standardized voice input information according to a part-of-speech classification standard to obtain a plurality of input word vectors; calculating similarity values between the input word vectors and a plurality of reference word vectors of each piece of instruction information in the voice interaction word bank according to the convolutional neural network, and providing corresponding feedback information for the user according to the similarity values;
and the execution module is used for executing the feedback task according to the feedback information output by the voice recognition module.
Further, the method for obtaining the standardized voice information by standardizing the collected voice information comprises the following steps:
step a: judging whether the input information acquired by the voice acquisition module comprises dialects, if so, converting the dialects in the input information into standard mandarin and then converting the whole section of input information into character information; otherwise, directly converting the input information into character information;
step b: judging whether the standard mandarin language characters contain foreign language information, if so, translating the foreign language information in the standard mandarin language characters into Chinese and then outputting standardized voice input information; otherwise, directly outputting the character information obtained in the step a as the standardized voice input information.
Further, when the maximum similarity value between the input information and the instruction information is larger than or equal to the maximum threshold value, directly outputting feedback information matched with the instruction information corresponding to the maximum similarity value; when the maximum similarity value between the input information and the instruction information is smaller than the maximum threshold value and larger than the minimum threshold value, extracting a reference word vector of the instruction information corresponding to the maximum similarity value, performing fuzzy sound conversion processing on the input word vector different from the extracted reference word vector to obtain fuzzy input information consisting of fuzzy input word vectors, calculating the maximum fuzzy similarity value between the fuzzy input word vector of the fuzzy input information and each instruction information, and when the maximum fuzzy similarity value is larger than or equal to the maximum threshold value, outputting feedback information matched with the instruction information corresponding to the maximum fuzzy similarity value; otherwise, outputting the input information invalid feedback information.
Further, when the maximum fuzzy similarity value between the fuzzy input word vector of the fuzzy input information and the instruction information is greater than or equal to the maximum threshold value, the current fuzzy input information replaces the instruction information with the maximum fuzzy similarity value in the voice interaction word library.
Further, a plurality of reference word vectors included in each instruction in the speech interaction lexicon respectively form a segment of sequence X, where the sequence X can be represented as:
wherein A is n Is a noun reference vector, B v For verb reference vectors, C a For adjective reference vectors, D num For reference vectors of words, E pron For quantifier reference vectors, F com As pronoun reference vectors, G emp Reference vectors for the particle.
Further, the input information includes a plurality of input word vectors respectively forming a segment of sequence Y, where Y may be represented as:
wherein the content of the first and second substances,
is a noun input vector, is asserted>
Inputs a vector for the verb, and>
entering a vector for an adjective, based on the location of the adjective in the frame, and based on the location of the adjective in the frame>
Inputting a vector for a number, and->
Inputting a vector for a quantifier, and->
Inputs a vector for a pronoun, and holds>
A vector is input for the particle.
The invention has the beneficial effects that: the robot decomposes the instruction information in the voice interaction word bank and the user voice input information into word vectors according to the part of speech; then, calculating a similarity value between the instruction information and the input information by using a convolutional neural network, and providing corresponding feedback information for a user according to the similarity value; according to the method and the device, fuzzy sound conversion processing can be carried out on the input information which cannot be successfully matched, then the input information which is subjected to the fuzzy sound conversion processing is matched with the instruction information, and the dialect voice information recognition efficiency can be improved.
Drawings
FIG. 1 is a schematic block diagram of one embodiment of the present invention.
Detailed Description
The intelligent voice interaction robot shown in fig. 1 comprises an intelligent interaction system, wherein the intelligent interaction system comprises a user image module, a database module, a voice acquisition module, a data processing module and an execution module.
The user image module is used for acquiring user registration information and sound information to establish a user image; the database module is used for constructing a robot voice interaction word bank according to the general robot instruction information and matching corresponding feedback information for each instruction information in the voice interaction word bank; performing word segmentation processing on each piece of instruction information in the voice interaction word bank according to a part-of-speech classification standard to obtain a plurality of reference word vectors; the voice acquisition module is used for acquiring voice information of a registered user and preprocessing the acquired voice information to obtain standardized voice input information; the data processing module is used for carrying out word segmentation processing on the standardized voice input information according to a part-of-speech classification standard to obtain a plurality of input word vectors; calculating similarity values between the input word vectors and a plurality of reference word vectors of each piece of instruction information in the voice interaction word bank according to the convolutional neural network, and providing corresponding feedback information for the user according to the similarity values; the execution module is used for executing the feedback task according to the feedback information output by the voice recognition module.
Decomposing instruction information in a speech interaction word bank and user speech input information into word vectors according to parts of speech by the applicant; then, calculating a similarity value between the instruction information and the input information by using a convolutional neural network, and providing corresponding feedback information for a user according to the similarity value; according to the method and the device, fuzzy sound conversion processing can be carried out on the input information which cannot be successfully matched, then the input information which is subjected to the fuzzy sound conversion processing is matched with the instruction information, and the dialect voice information recognition efficiency can be improved.
According to an embodiment of the present application, the method for obtaining the standardized speech information by normalizing the collected speech information includes:
step a: judging whether the input information acquired by the voice acquisition module comprises dialects, if so, converting the dialects in the input information into standard mandarin and then converting the whole section of input information into character information; otherwise, directly converting the input information into character information;
step b: judging whether the standard mandarin language characters contain foreign language information, if so, translating the foreign language information in the standard mandarin language characters into Chinese and then outputting standardized voice input information; otherwise, directly outputting the character information obtained in the step a as the standardized voice input information.
According to one embodiment of the application, when the maximum similarity value between the input information and the instruction information is larger than or equal to the maximum threshold value, the feedback information matched with the instruction information corresponding to the maximum similarity value is directly output; when the maximum similarity value between the input information and the instruction information is smaller than the maximum threshold value and larger than the minimum threshold value, extracting a reference word vector of the instruction information corresponding to the maximum similarity value, performing fuzzy sound conversion processing on the input word vector different from the extracted reference word vector to obtain fuzzy input information consisting of fuzzy input word vectors, calculating the maximum fuzzy similarity value between the fuzzy input word vector of the fuzzy input information and each instruction information, and when the maximum fuzzy similarity value is larger than or equal to the maximum threshold value, outputting feedback information matched with the instruction information corresponding to the maximum fuzzy similarity value; otherwise, outputting the input information invalid feedback information.
According to one embodiment of the application, when the maximum fuzzy similarity value of the fuzzy input word vector of the fuzzy input information and the instruction information is larger than or equal to the maximum threshold value, the current fuzzy input information replaces the instruction information with the maximum fuzzy similarity value in the voice interaction word library.
According to an embodiment of the present application, a plurality of reference word vectors included in each instruction in the speech interaction lexicon respectively form a sequence X, where the sequence X can be represented as:
wherein A is n Is a noun reference vector, B v For verb reference vectors, C a For adjective reference vectors, D num For reference vectors of words, E pron For quantifier reference vectors, F com As pronoun reference vectors, G emp Reference vectors for the particle.
According to an embodiment of the present application, the input information includes a plurality of input word vectors respectively forming a segment of sequence Y, where Y may be represented as:
wherein the content of the first and second substances,
is a noun input vector, is asserted>
Inputs a vector for the verb, and>
inputting a vector for an adjective, and->
Inputting a vector for a number, and->
Entering a vector for a quantifier, based on a predetermined criterion>
Inputs a vector for a pronoun, and holds>
A vector is input for the particle.
The convolutional neural network comprises an attention layer, a convolutional layer, a pooling layer and an output layer; the input of attention layer is input word vector and reference word vector, the input layer of convolution layer is attention layer, carries out two-dimentional convolution to the output of attention layer, the pooling layer is the pooling of convolution layer, the output layer adopts the softmax function to produce the classification.
Finally, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that various changes and modifications may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (4)
1. An intelligent voice interaction robot, comprising an intelligent interaction system, wherein the intelligent interaction system comprises:
the user image module is used for acquiring user registration information and sound information to establish a user image;
the database module is used for constructing a robot voice interaction word bank according to the general robot instruction information and matching corresponding feedback information for each instruction information in the voice interaction word bank; performing word segmentation processing on each piece of instruction information in the voice interaction word bank according to a part-of-speech classification standard to obtain a plurality of reference word vectors;
the voice acquisition module is used for acquiring voice information of a registered user and preprocessing the acquired voice information to obtain standardized voice input information;
the data processing module is used for carrying out word segmentation processing on the standardized voice input information according to a part-of-speech classification standard to obtain a plurality of input word vectors; calculating similarity values between the input word vectors and a plurality of reference word vectors of each piece of instruction information in the voice interaction word bank according to the convolutional neural network, and providing corresponding feedback information for the user according to the similarity values;
the execution module is used for executing a feedback task according to the feedback information output by the voice recognition module;
the method for standardizing the collected voice information to obtain standardized voice information comprises the following steps:
step a: judging whether the input information acquired by the voice acquisition module comprises a dialect, if so, converting the dialect in the input information into standard Mandarin and then converting the whole section of input information into character information; otherwise, directly converting the input information into character information;
step b: judging whether the standard mandarin language characters contain foreign language information, if so, translating the foreign language information in the standard mandarin language characters into Chinese and then outputting standardized voice input information; otherwise, directly outputting the character information obtained in the step a as standardized voice input information;
when the maximum similarity value between the input information and the instruction information is larger than or equal to the maximum threshold value, directly outputting feedback information matched with the instruction information corresponding to the maximum similarity value; when the maximum similarity value between the input information and the instruction information is smaller than the maximum threshold value and larger than the minimum threshold value, extracting a reference word vector of the instruction information corresponding to the maximum similarity value, performing fuzzy sound conversion processing on the input word vector different from the extracted reference word vector to obtain fuzzy input information consisting of fuzzy input word vectors, calculating the maximum fuzzy similarity value between the fuzzy input word vector of the fuzzy input information and each instruction information, and when the maximum fuzzy similarity value is larger than or equal to the maximum threshold value, outputting feedback information matched with the instruction information corresponding to the maximum fuzzy similarity value; otherwise, outputting the input information invalid feedback information.
2. The intelligent voice interaction robot of claim 1, wherein when the maximum fuzzy similarity value between the fuzzy input word vector of the fuzzy input information and the instruction information is greater than or equal to a maximum threshold value, the current fuzzy input information is made to replace the instruction information with the maximum fuzzy similarity value in the voice interaction lexicon.
3. The intelligent voice interaction robot of claim 1, wherein a plurality of reference word vectors included in each instruction in the voice interaction lexicon respectively form a sequence X, wherein the sequence X can be represented as:
wherein A is n Is a noun reference vector, B v Is a verb reference vector, C a For adjective reference vectors, D num Reference vector for number, E pron Reference vector for quantifier, F com As pronoun reference vectors, G emp Reference vectors for the particle.
4. The intelligent voice interaction robot of claim 3, wherein the input information comprises a plurality of input word vectors respectively forming a sequence Y, wherein Y can be expressed as:
wherein the content of the first and second substances,
for a noun input vector, based on a number of words in a set of words>
Inputs a vector for the verb, and>
inputting a vector for an adjective, and->
Inputting a vector for a number, and->
Is measured byWord input vector, <' > based on>
Inputs a vector for a pronoun, and holds>
Vectors are input for the particle. />
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