CN114360518A - Voice interaction method and device, server and readable storage medium thereof - Google Patents
Voice interaction method and device, server and readable storage medium thereof Download PDFInfo
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Abstract
The invention discloses a voice interaction method and device, a server and a readable storage medium. The voice interaction method comprises the following steps: receiving a voice request of a current wheel, which is forwarded by a vehicle and is adjusted by a preset function of the vehicle, wherein the preset function is a function of simulating the operation of vehicle parts to carry out scale adjustment; reading a voice request of a previous round of adjusting a preset function of the vehicle; rewriting the voice request of the current round by using the voice request of the previous round; performing precision recognition on the rewritten voice request of the current wheel; and finishing voice interaction according to the result of the precision recognition. The invention identifies the scale adjustment precision of the voice request by combining the instruction rewriting and precision identification, and realizes the accurate identification of the scale adjustment range under multiple rounds of voice requests.
Description
Technical Field
The present invention relates to the field of voice technologies, and in particular, to a voice interaction method and apparatus, a server, and a readable storage medium.
Background
At present, in an intelligent automobile scene, voice interaction can be applied to realize control of a user on vehicle hardware equipment, such as 'window opening', 'volume up' and the like, however, for a scene that the user wishes to perform continuous adjustment, the scene is embodied as multi-turn interaction in the voice scene, and the user naturally omits part of contents of each subsequent turn of conversation after the last turn of voice interaction, for example, the following conversation of the user with a voice assistant small P:
the user: how do the weather today?
Small P: guangzhou today is 26-30 ° in sunny days.
The user: shanghai (weather)?
In a multi-turn conversation, like the first example, the user literally asks Shanghai, but wants to ask weather in Shanghai, and omits part of the content to conform to the habit of human conversation, but this may cause the voice request of the vehicle-mounted system of the vehicle to be not accurately recognized for certain turns or the prompt is not understood.
Further, if the user needs to adjust the volume, the mechanical knob for adjusting the volume of the car on the car can be operated to rotate to the desired volume, but if the volume is adjusted by voice, the volume can only be adjusted up or down. In a second example as follows:
the user: volume is turned up greatly
Small P: volume is adjusted greatly
The user: big
As can be seen from the second example, the current vehicle-mounted system cannot accurately identify the size of the current wheel or the prompt cannot be understood, so that the requirement of the user for continuous adjustment of the scale as accurate as a mechanical knob cannot be met.
Disclosure of Invention
The embodiment of the invention provides a voice interaction method and device, a server and a readable storage medium.
The embodiment of the invention provides a voice interaction method. The voice interaction method comprises the following steps: receiving a voice request of a current wheel, which is forwarded by a vehicle and is adjusted by a preset function of the vehicle, wherein the preset function is a function of simulating the operation of vehicle parts to carry out scale adjustment; reading a voice request of a previous round of adjusting a preset function of the vehicle; rewriting the voice request of the current round by using the voice request of the previous round; performing precision recognition on the rewritten voice request of the current wheel; and finishing voice interaction according to the result of the precision recognition.
Therefore, after receiving a voice request of a user for a preset function of the vehicle, the voice interaction method can rewrite the voice request of the current wheel by reading the voice request of the previous wheel and utilizing the voice request of the previous wheel so that the rewritten voice request of the current wheel can be recognized by a vehicle-mounted system of the vehicle to obtain corresponding scale adjustment precision, and further scale adjustment of vehicle parts can be realized in a voice interaction mode according to the precision recognition result. The scale adjustment precision of the voice request is identified in a mode of combining instruction rewriting and precision identification, and accurate identification of the scale adjustment range under multiple rounds of voice requests is achieved.
The voice interaction method comprises the step of reversely mining two adjacent rounds of voice requests with the occurrence frequency larger than the preset frequency to construct a regular engine.
Therefore, the regular engine construction of the high-frequency set voice request is realized by reversely mining two adjacent voice request constructions with the occurrence frequency higher than the preset frequency.
After the voice request of the previous round of the preset function adjustment of the vehicle is read, the voice interaction method comprises the following steps: utilizing a regular engine to identify the scale adjustment precision corresponding to the voice request of the current round and the voice request of the previous round; and completing voice interaction according to the recognized scale regulation precision under the condition that the recognition result of the regular engine is that the corresponding scale regulation precision is recognized.
Therefore, the invention combines the current round of voice request and the previous round of voice request to carry out the recognition of the regular engine, and can directly determine the corresponding scale adjustment precision for the high-frequency rule, thereby completing the voice interaction according to the determined scale adjustment precision.
The rewriting of the voice request of the current round by the voice request of the previous round comprises: and under the condition that the recognition result of the regular engine is that the corresponding scale adjustment precision cannot be recognized, rewriting the voice request of the current round by using the voice request of the previous round.
Therefore, when the regular engine cannot recognize the scale adjustment precision of the voice request of the current round according to the voice request of the previous round and the voice request of the current round, the voice request of the current round can be rewritten by using the voice request of the previous round, so that the rewritten voice request of the current round can be recognized by a vehicle-mounted system of a vehicle to have the corresponding scale adjustment precision.
The rewriting of the voice request of the current round by the voice request of the previous round comprises: obtaining a rewriting model through rewriting training data training, wherein the rewriting training data comprises two adjacent rounds of voice requests; and rewriting the voice request of the current round by using the voice request of the previous round and the rewriting model.
Therefore, the invention obtains the rewriting model through the training of the voice requests of two adjacent rounds in a machine learning mode, thereby realizing the rewriting of the voice request of the current round according to the voice request of the previous round and the rewriting model, and enabling the rewritten voice request to be recognized by a vehicle-mounted system of the vehicle to have corresponding scale adjustment precision.
The performing precision recognition on the rewritten voice request of the current round comprises: training precision training data to obtain a precision recognition model, wherein the precision training data is related to a vehicle part capable of being subjected to scale adjustment, a scale adjustment range of the vehicle part and a scale adjustment precision range of the part; and performing precision recognition on the rewritten voice request of the current wheel by using the precision recognition model.
Therefore, the invention trains the vehicle parts capable of scale adjustment, the scale adjustment range of the vehicle parts and the training data corresponding to the scale adjustment precision range of the parts to obtain the precision recognition model in a machine learning mode, and further performs precision recognition on the rewritten voice request, thereby realizing accurate recognition of the scale adjustment precision corresponding to the voice request of the user.
The finishing of voice interaction according to the result of the precision recognition comprises the following steps: obtaining precision judging probability of the precision identification result corresponding to each preset scale adjustment precision; and determining the preset scale adjustment precision with the precision discrimination probability larger than the probability threshold as the target scale adjustment precision corresponding to the voice request so as to complete voice interaction.
Therefore, the voice interaction method can obtain the precision discrimination probability of the precision recognition result corresponding to each preset scale adjustment precision, and determines the preset scale adjustment precision with the precision discrimination probability larger than the probability threshold value as the target scale adjustment precision corresponding to the voice request, so that the scale adjustment precision of the vehicle part accurately adjusted by a user is recognized.
The voice interaction method comprises the following steps: and under the condition that the precision judging probability of each preset scale adjusting precision is not greater than a probability threshold, determining that the precision identification of the voice request of the current wheel is wrong.
Therefore, under the condition that the precision judging probability of each preset scale adjustment precision is not greater than the probability threshold, the voice request precision recognition error is determined, and the voice requests related to the non-scale adjustment precision can be eliminated.
The invention also provides a voice interaction device. The voice interaction device comprises: the device comprises an instruction receiving module, an instruction reading module, a rewriting module, a precision identification module and an interaction module. The receiving instruction module is used for receiving a voice request of a current wheel, forwarded by a vehicle, for adjusting a preset function of the vehicle, wherein the preset function is a function of simulating scale adjustment of operation of vehicle parts; the reading instruction module is used for reading a voice request of a previous round of adjusting the preset function of the vehicle; the rewriting module is used for rewriting the voice request of the current round by using the voice request of the previous round; the precision recognition module is used for carrying out precision recognition on the rewritten voice request of the current wheel; and the interaction module is used for finishing voice interaction according to the result of the precision recognition.
Therefore, after receiving the voice request of the user for the preset function of the vehicle, the voice interaction device can read the voice request of the previous round, and rewrite the voice request of the current round by using the voice request of the previous round so that the rewritten voice request can be recognized by a vehicle-mounted system of the vehicle to obtain the corresponding scale regulation precision, and further realize the scale regulation of the vehicle parts in a voice interaction mode according to the precision recognition result. The scale adjustment precision of the voice request is identified in a mode of combining instruction rewriting and precision identification, and accurate identification of the scale adjustment range under multiple rounds of voice requests is achieved.
The invention provides a server. The server comprises a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the voice interaction method of any one of the above embodiments is realized.
Therefore, the server executes the computer program through the processor, can rewrite the voice request of the current wheel by utilizing the voice request of the previous wheel by reading the voice request of the previous wheel after receiving the voice request of the user for the preset function of the vehicle, so that the rewritten voice request can be recognized by a vehicle-mounted system of the vehicle to have corresponding scale adjustment precision, and further realizes scale adjustment of vehicle parts in a voice interaction mode according to the precision recognition result. The scale adjustment precision of the voice request is identified in a mode of combining instruction rewriting and precision identification, and accurate identification of the scale adjustment range of a user under multiple rounds of voice requests is achieved.
The embodiment of the invention also provides a nonvolatile computer readable storage medium containing the computer program. The computer program, when executed by one or more processors, implements the voice interaction method of any of the above embodiments.
Thus, when the computer program stored in the readable storage medium of the present invention is executed by the processor, after receiving a voice request of a user for a preset function of a vehicle, the voice request of a previous round is read, and the voice request of the previous round is used to rewrite the voice request of a current round, so that the rewritten voice request can be recognized by a vehicle-mounted system of the vehicle to obtain a corresponding scale adjustment precision, and further scale adjustment of vehicle parts can be performed in a voice interaction manner according to a precision recognition result. The scale adjustment precision of the voice request is identified in a mode of combining instruction rewriting and precision identification, and accurate identification of the scale adjustment range under multiple rounds of voice requests is achieved.
Additional aspects and advantages of embodiments of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flow chart diagram of a voice interaction method of the present invention;
FIG. 2 is a schematic diagram of the structure of the voice interaction device of the present invention;
FIG. 3 is a flow chart diagram of a voice interaction method of the present invention;
FIG. 4 is a schematic structural diagram of a voice interaction apparatus of the present invention;
FIG. 5 is a flow chart diagram of a voice interaction method of the present invention;
FIG. 6 is a flow chart diagram of a voice interaction method of the present invention;
FIG. 7 is a schematic diagram of a mapping module in the voice interaction apparatus according to the present invention;
FIG. 8 is a flow chart diagram of a voice interaction method of the present invention;
FIG. 9 is a schematic diagram of the structure of the voice interaction apparatus of the present invention;
FIG. 10 is a flow chart diagram of a voice interaction method of the present invention;
FIG. 11 is a flow chart diagram of a voice interaction method of the present invention;
FIG. 12 is a schematic diagram of the structure of the voice interaction apparatus of the present invention;
FIG. 13 is a flow chart diagram of a voice interaction method of the present invention;
FIG. 14 is a schematic structural diagram of a voice interaction device of the present invention;
FIG. 15 is a flow chart diagram of a voice interaction method of the present invention;
FIG. 16 is a schematic structural diagram of an interaction module in the voice interaction apparatus of the present invention;
FIG. 17 is a schematic diagram of the architecture of the server of the present invention;
fig. 18 is a schematic structural diagram of a computer-readable storage medium of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are exemplary only for the purpose of illustrating the embodiments of the present invention and are not to be construed as limiting the embodiments of the present invention.
At present, under the condition that a user carries out multiple rounds of voice requests, for example, a first round of voice requests of the user is screen brightening, and a second round of voice requests adopts simplified voice requests of brightness, the voice interaction system cannot accurately identify the requirement of the second round of voice requests of the user according to the voice requests of the user to improve the screen brightness by 3 scales, and cannot accurately issue instructions from a vehicle end, so that the screen brightness accurately improves three brightness required by the user, and the user experience is poor.
To solve the above problem, please refer to fig. 1, the present invention provides a voice interaction method. The voice interaction method comprises the following steps:
01, receiving a voice request of a current wheel, which is forwarded by a vehicle, for adjusting a preset function of the vehicle, wherein the preset function refers to a function of simulating the operation of vehicle parts for scale adjustment;
02, reading a voice request of the previous round of the preset function adjustment of the vehicle;
03, rewriting the voice request of the current round by using the voice request of the previous round;
04, performing precision recognition on the rewritten voice request of the current wheel;
and 05, finishing voice interaction according to the result of the precision recognition.
Referring to fig. 2, the present invention further provides a voice interaction apparatus 10. The voice interaction apparatus 10 includes: the system comprises an instruction receiving module 11, an instruction reading module 12, a rewriting module 13, a precision identification module 14 and an interaction module 15.
The user uses voice interaction to simulate the process of adjusting the scales of the vehicle parts, and the corresponding voice requests can include but are not limited to 'bright and bright screen', 'big and big volume', 'back and forth behind the seat'. The preset function refers to a function of adjusting the scale through vehicle parts, wherein the vehicle parts may refer to physical parts such as mechanical knobs or buttons, and these parts are parts capable of adjusting the scale. At present, in an intelligent automobile, for a scene that a user wishes to perform continuous adjustment, multi-round interaction is embodied in a voice scene, for example, a previous round of voice request of the user is 'big volume', after the volume of a system is adjusted to be big, the user sends a voice request of a current round to be 'little', and at this time, the system can not accurately recognize that specific scales are 2 scales for volume reduction aiming at a second round of voice request; only the user is prompted to be unable to identify the current instruction or simply and wrongly adjust the wrong hardware and the like, and the requirement of the user on continuous adjustment of the scales as accurate as a mechanical knob cannot be met.
The voice interaction method can rewrite the voice request of the current round by reading the voice request of the previous round after receiving the voice request of the user for the preset function of the vehicle, so that the rewritten voice request can be recognized by a vehicle-mounted system of the vehicle to have corresponding scale adjustment precision, and then sends a control instruction according to the precision recognition result to control the corresponding vehicle part to complete voice interaction. The scale adjustment precision of the voice request is recognized in a mode of combining instruction rewriting and precision recognition, and the scale adjustment precision of the scale adjustment is realized by accurately recognizing the operation of simulating vehicle parts through voice interaction under the condition of realizing multiple rounds of voice requests.
It should be noted that, after receiving a voice request of a user for a current wheel of a vehicle preset function, performing voice recognition on the received voice request of the current wheel to obtain a voice recognition text of the current wheel for subsequent processing, for example, performing voice recognition on a "screen bright" voice request of the current wheel with an adjustment to the vehicle preset function input by the user, and obtaining the current wheel recognition text as "screen bright".
In practical situations, the text instruction after ASR recognition may not be clear and accurate enough due to vehicle hardware limitations or network instability, user speaking or speaking, and the like, and the received voice request of the current wheel may be preprocessed, where the preprocessing includes some conventional text error correction, such as "volume depth and depth" correction to "volume increase and increase", and some removal of meaningless words, such as "o", "please", and the like.
Referring to fig. 3, before step 01, the voice interaction method includes:
011, determining the control range and the non-control range of the vehicle parts.
Referring to fig. 4, the voice interaction apparatus 10 further includes a first determining module 111.
Step 011 can be implemented by the first determining module 111. That is, the first determination module 111 may be used to determine the control range and the non-control range of the vehicle component.
It will be appreciated that not all functional adjustments of the vehicle are possible, capable or desirable to make precise scale adjustments. For example, movement of the seat in various directions may be adjusted by vehicle components. The door has no vehicle parts like knobs and buttons to realize scale adjustment, and is usually opened and closed only by a door handle. Thus, seat adjustments are within the control range of the vehicle component, while door adjustments are within the non-control range of the vehicle component.
The method comprises the steps of obtaining information of vehicle parts, determining hardware which can be subjected to scale adjustment through the vehicle parts according to the information of the vehicle parts, determining the hardware as a control range of the vehicle parts, and determining the hardware which cannot be subjected to scale adjustment through the vehicle parts as a non-control range.
First, the parts on the vehicle that can be adjusted in scale are determined, for example: a volume knob, a screen brightness button, an air conditioner air volume knob/button, a seat adjusting knob/button and the like. Further, determining the control range of the vehicle component may include: a car audio, a screen in a vehicle, a vehicle air conditioner, a vehicle seat, an atmosphere lamp in a vehicle, a lamp outside a vehicle, a window, or the like. The non-control ranges of the vehicle components may include: doors, mirrors, trunks, etc.
During subsequent voice interactions, voice prompts may be presented with a voice request for a non-control range of a vehicle component.
Thus, the control range of the vehicle parts, that is, the control range in which the scale adjustment can be performed through voice interaction, is determined by collecting the vehicle part information and confirming the function in which the scale adjustment can be performed through the parts.
The voice interaction method comprises the following steps:
012, determining the adjustable range of the vehicle component.
The voice interaction device 10 also includes a second determination module 112.
Step 012 may be implemented by the second determination module 112. That is, the second determination module 112 may be used to determine the adjustable range of the vehicle component.
It will be appreciated that after determining the control range and the non-control range of the vehicle component, the adjustable range needs to be determined for each vehicle component in the control range. The adjustable range of the vehicle part corresponds to a scale range adjusted by operating the vehicle part. The adjustable range may be a gear or a range for different vehicle components. For example, the screen brightness button is pressed 5 times in an accumulated manner, the screen brightness is sequentially adjusted to the maximum brightness from 1 to 5 gears, and the adjustable range of the screen brightness button is 1 to 5 gears. If the total scale value of the knob for adjusting the seat back and forth is 90, the adjustable range of the seat adjusting knob is 1-90.
Referring to fig. 5, step 012 includes:
0121, determining the adjustable range of the vehicle parts corresponding to the simplified words.
Specifically, the simplified word refers to a word which is used by the user and can accurately represent the adjustment degree, for example, a superimposed word can be used as the simplified word, so that the user only needs to input a simplified instruction when inputting a voice instruction. For example, brightness adjustment of the vehicle-mounted display screen can be simply expressed as "screen bright", "screen dark" and "screen dark" …, volume adjustment of the vehicle-mounted stereo can be simply expressed as "volume large", "volume small" and "volume small" …, and air volume adjustment of the air conditioner can be simply expressed as "air volume large", "air volume small" and "air volume small" …. Of course, the simplified words may be repeated words that the user is accustomed to using, such as "light", "dark", "big", and "small", and accordingly, the user voice request may be expressed in a simplified manner as "light on screen", "dark on screen", "big in volume", and "small in volume", and the like, and is not limited in detail herein.
The adjustable range corresponding to the reduced words can be determined according to the adjustable range of the vehicle parts. For example, when a screen in a vehicle is adjusted, the corresponding adjustable range of the screen brightness is 1-5 gears, at most 5 simplified words can be recognized in each voice request related to the brightness during voice recognition, and the adjustable range of the simplified words can be 1-5. When the voice request comprises a plurality of simplified words, each simplified word can adjust 1 gear of the screen brightness.
For another example, when the car audio is adjusted, the volume may be adjusted, that is, the simplified words "large", "small", or "small" may be used for adjustment, the total adjustment range of the volume is 30 scales, and the voice request related to the volume during voice recognition may identify at most 10 simplified words, at this time, the adjustable range of the simplified words may be 1 to 10, and each corresponding simplified word may adjust 3 scales of the volume of the car audio. If the voice recognition has more than 10 voice requests of the simplified word, the volume can be directly adjusted to be maximum or minimum.
The voice interaction method comprises the following steps:
013, mapping the control range and the adjustable range to the corresponding preset scale adjustment precision.
Step 013 can be implemented by the mapping module 113. That is, the mapping module 113 may be configured to map the control range and the adjustable range to corresponding preset scale adjustment accuracies.
For the preset scale adjustment precision, for example, the volume is adjusted by 3 scale values each time when voice interaction simulation is performed on the operation of the vehicle parts, the total scale value is 30, and the preset scale adjustment precision range can be 1-10. For another example, when the voice interaction simulates the operation of the vehicle parts, 18 scales are adjusted at each time before and after the seat, and the total scale value is 90, the preset scale adjustment precision range is 1-5.
Referring to fig. 6, step 013 includes:
0131, setting the simplified words as slot positions, and extracting the slot positions of the preset identification texts corresponding to the vehicle parts to obtain repeated fields;
0132, repeatedly counting the slot values of the repeated fields to obtain a repeated number;
0133, the repetition number is mapped to the preset scale adjustment precision according to the adjustable range of the simplified words.
Referring to fig. 7, the mapping module 113 includes an extracting unit 1131, a counting unit 1132, and a mapping unit 1133.
It will be appreciated that the number of repetitions of the reduced word may represent the number of scale adjustments made to the vehicle component. Therefore, the condensed word may be set as a slot. For example, the adjustable range of the simplified words of the volume knob is 1-10, the adjustment precision range of the preset scales corresponding to the volume knob is 1-10, and if the preset identification text corresponding to the voice request is 'volume is greatly increased', the 'volume is greatly increased' can be extracted as a slot position, and the slot position is set as a repeated field. Then, the slot value of the extracted repeated field is repeatedly counted, the number of the repeated fields is mapped to the corresponding preset scale adjustment precision, and for the extracted slot position 'big', the number of the big 'repeated fields is 4, and the big' repeated fields can be mapped to the corresponding preset scale adjustment precision 4.
In other embodiments of the present invention, different user instructions may be collected with respect to the same scale adjustment accuracy when the user allows, for example, in the case of "volume up", the user may expand with different degrees of freedom, for example, "volume up" or "volume up", or "volume up", and the scale adjustment accuracy obtained by identifying different expansion words is "volume adjustment 3 times".
Referring to fig. 8, the voice interaction method includes:
014, reverse mining is carried out on two adjacent rounds of voice requests with the occurrence frequency larger than the preset frequency to construct a regular engine.
Referring to FIG. 9, the speech interaction device 10 includes a regularization engine module 114.
Step 014 may be implemented by the regularization engine module 114. That is, the regularization engine module 114 may be configured to reverse mine two adjacent rounds of voice requests having a frequency of occurrence greater than a predetermined frequency to construct the regularization engine.
Therefore, the regular engine construction of the high-frequency set voice request is realized by reversely mining the two adjacent voice request mechanisms with the occurrence frequency greater than the preset frequency.
First, the server can collect the historical voice information of the user in a period of time under the permission of the user, and the collected voice request needs to at least comprise two voice requests. Where it is expected that more than 1 million pieces of historical speech information need to be collected.
Secondly, the server can simply screen the collected historical voice information to screen out voice information with obvious undefined semantics and short voice information only containing words such as 'o' and the like, and leave voice information with definite semantics and specific purposes such as 'navigation to north,' help me open an air conditioner ',' search nearby hospitals ',' play Zhou Jie 'songs', 'what weather is today' and the like; and only one round of voice requests is removed from the screening.
Then, the server can perform high-frequency set statistics on the screened voice requests, and collects high-frequency rules and constructs a regular engine by reversely mining extractable extraction templates of two adjacent voice requests with the occurrence frequency greater than the preset frequency. The occurrence frequency may refer to the number of occurrences of two corresponding adjacent voice requests in the collected voice information, and when the number of occurrences is greater than a certain number, it may be determined that the usage frequency of two corresponding adjacent voice requests is greater than a preset frequency.
For example, a first round of voice requests is "volume big", a second round of voice requests is "little", and the first round of voice requests is mapped to a rule "(volume) xxx (little) by reverse mining, wherein the middle parenthesis represents that" little can be matched with one or more repetitions ", thereby establishing the regular engine through each high-frequency rule.
The voice interaction method comprises the following steps:
015, recognizing the scale adjustment precision corresponding to the current round of voice request and the previous round of voice request by using a regular engine;
016, when the recognition result of the regular engine is that the corresponding scale adjustment precision is recognized, completing the voice interaction according to the recognized scale adjustment precision.
Step 015 may be implemented by the regularization engine module 114 and step 016 may be implemented by the interaction module 15. That is, the regularization engine module 114 may be configured to identify, with the regularization engine, a scale adjustment precision corresponding to a current round of voice requests and a previous round of voice requests. The interaction module 15 may be configured to complete voice interaction according to the identified scale adjustment accuracy when the identification result of the regular engine identifies the corresponding scale adjustment accuracy.
Therefore, in the regular engine, whether the current round of voice requests and the previous round of voice requests belong to the high-frequency set instruction can be determined by identifying, and the regular engine can identify the corresponding scale adjustment precision under the condition that the current round of voice requests and the previous round of voice requests belong to the high-frequency set instruction, so that voice interaction can be completed according to the identified scale adjustment precision.
For example, if the first round of voice requests is "volume is a little larger than a little larger", and the second round of voice requests is "little smaller than a little smaller", then the first round of voice requests can be mapped to rule "(volume) xxx [ little ] x 2", and if the rule is a rule corresponding to the high frequency set instruction, the regular engine can recognize that "little" is repeated for 2 times, so that the corresponding scale adjustment range is "volume adjustment 2 times", and the operation of vehicle parts is simulated through voice interaction, thereby realizing accurate recognition of scale adjustment accuracy under multiple rounds of voice requests.
Referring to fig. 10, step 03 includes:
031, in case that the recognition result of the regular engine is that the corresponding scale adjustment precision cannot be recognized, rewriting the voice request of the current round by using the voice request of the previous round.
Step 031 may be implemented by rewrite module 13. That is, the rewriting module 13 may be configured to rewrite the voice request of the current round with the voice request of the previous round when the recognition result of the regular engine is that the corresponding scale adjustment accuracy cannot be recognized.
It can be understood that, under the condition that the recognition result of the regular engine is that the corresponding scale adjustment accuracy cannot be recognized, the voice request of the current round and the voice request of the previous round are non-high frequency set instructions or belong to composite instructions, the scale adjustment accuracy of the voice request of the current round is not easy to be recognized by the regular engine, and at the moment, the voice request of the current round can be rewritten, so that the rewritten voice request can be recognized by a vehicle-mounted system of a vehicle to obtain the corresponding scale adjustment accuracy.
Referring to fig. 11, step 03 includes:
032, obtaining a rewriting model by rewriting training data, wherein the rewriting training data comprises two adjacent voice requests;
033, rewriting the voice request of the current round by using the voice request of the previous round and the rewriting model.
Referring to fig. 12, the voice interaction device 10 includes an adaptation training module 115.
Step 032 may be implemented by rewrite training module 115, and step 033 may be implemented by rewrite module 13. That is, the rewrite training module 115 may be configured to train to obtain a rewrite model by rewriting training data. The rewrite module 13 may be configured to rewrite the voice request of the current round with the voice request of the previous round and a rewrite model.
Therefore, the invention obtains the rewriting model through the training of the voice requests of two adjacent rounds in a machine learning mode, thereby realizing the rewriting of the voice request of the current round according to the voice request of the previous round and the rewriting model, and enabling the rewritten voice request to be recognized by a vehicle-mounted system of the vehicle to have corresponding scale adjustment precision. For the rewriting model, model training may be performed using bert (bidirectional Encoder replication from transformations) and sequence labeling, so as to obtain a trained rewriting model.
The rewriting data may be obtained by labeling two adjacent voice requests in the screened voice requests, and specifically, the rewriting labeling may be performed manually on a second voice request in the two adjacent voice requests, for example, if the first voice request is "a little bit louder in volume", and the second voice request is "a little bit smaller in volume", then the second voice request may be rewritten and labeled as "a little bit louder in volume". Therefore, the marked adjacent two-round voice requests are sent to the established rewriting model, and in the training process, the rewriting model can learn how to rewrite the second round voice requests into the marked second round voice requests through the adjacent two-round voice requests through feature extraction.
In the training process, two adjacent voice requests in the marked voice information are divided into a rewriting training set and a rewriting verification set, and the division ratio can be set according to requirements, which is not limited herein. For example, the rewrite training set is 80% and the rewrite validation set is 20%. And for the established rewriting model, firstly using at least part of data in the rewriting training set for training the rewriting model, and then using at least part of data in the rewriting verification set for rewriting and verifying the accuracy of the trained rewriting model. And under the condition that the accuracy of the rewriting verification does not reach the rewriting accuracy threshold, training the rewriting model again through at least another part of data of the rewriting training set, and rewriting and verifying the accuracy of the rewritten model after the re-training by using at least another part of data of the rewriting verification set again.
It should be noted that each data in the rewrite training set and the rewrite verification set is used only once, and when all data in the rewrite model traversing the rewrite training set and the rewrite verification set are not trained to reach the standard, more voice information can be collected again under the condition of user permission, so that more rewrite training data obtained by screening and labeling are trained on the rewrite model, and the rewrite model is ensured to be capable of accurately rewriting the voice request.
Referring to fig. 13, step 04 includes:
041, training precision training data to obtain a precision recognition model, wherein the precision training data is related to the vehicle parts which can be subjected to scale adjustment through the vehicle parts, the scale adjustment range of the vehicle parts and the scale adjustment precision range of the parts;
042, the accuracy recognition model is used to perform accuracy recognition on the rewritten voice request of the current round.
Referring to fig. 14, the speech interaction device 10 includes an accuracy training module 116.
Step 041 may be implemented by precision training module 116 and step 042 may be implemented by precision identification module 14. That is, the precision training module 116 may be used to train the precision recognition model through the precision training data. The accuracy recognition module 14 may be configured to perform accuracy recognition on the rewritten current round of voice requests using an accuracy recognition model.
Therefore, the invention trains the vehicle parts capable of scale adjustment, the scale adjustment range of the vehicle parts and the training data corresponding to the scale adjustment precision range of the parts to obtain the precision recognition model in a machine learning mode, and further performs precision recognition on the rewritten voice request of the current wheel, thereby realizing accurate recognition of the scale adjustment precision of the user. Wherein, model training can utilize BERT, ALBERT, XLNET, RoBERTA and other models.
The precision training data is related to the vehicle parts and parts with scales adjustable through the vehicle parts and the scale adjusting range of the parts, and means that the precision training data comprises all the vehicle parts and parts with scales adjustable in the vehicle, such as a volume knob, a screen brightness button, an air conditioner air volume knob/button, a seat adjusting knob/button and the like. The adjustable range of the vehicle part corresponds to a scale range that is adjusted by operating the vehicle part. The scale adjusting range can be a gear or a measuring range corresponding to different vehicle parts, and the scale adjusting precision range can be a scale value adjusted each time.
The precision training data may be obtained by labeling the voice requests in the voice information after the screening, specifically, precision labeling may be performed on the voice requests in two adjacent voice requests manually, and it can be understood that the voice requests include contents related to the scale adjustment precision that the user needs to adjust, for example, the voice request is "the volume is larger by one, the user needs to adjust the volume by 2 times, and at this time, the scale adjustment precision corresponding to the round voice request may be labeled" the volume is adjusted by 2 times manually. Therefore, the marked voice request is sent to the established precision recognition model, and in the training process, the precision recognition model can learn how to recognize the target scale adjustment precision which the user wants to realize through the input voice request through feature extraction.
In the training process, the labeled voice request can be divided into a precision training set and a precision verification set, and the division ratio can be set according to requirements, which is not limited herein. For example, the precision training set is 80% and the precision verification set is 20%. And for the established precision recognition model, firstly, at least part of data in the precision training set is used for training the precision recognition model, and then, at least part of data in the precision verification set is used for carrying out precision verification on the accuracy of the trained precision recognition model. And under the condition that the accuracy of the accuracy verification does not reach the accuracy threshold, training the accuracy recognition model again through at least another part of data of the accuracy training set, and performing accuracy verification on the accuracy of the accuracy recognition model after the re-training by using another part of data of the accuracy verification set again, repeating the training and accuracy verification processes in such a way, and finishing the training of the accuracy recognition model after the accuracy of the accuracy verification reaches the accuracy threshold.
It should be noted that each data in the precision training set and the precision verification set is used only once, and under the condition that the precision recognition model traverses all data in the precision training set and the precision verification set, which are not trained to reach the standard, more voice information can be collected again under the condition that the user allows, so that more precision training data obtained by screening and labeling are used for training the precision recognition model, and the precision recognition model can be ensured to accurately recognize the scale adjustment precision corresponding to the input voice request.
It is understood that the above-mentioned training of the rewrite model and the accuracy recognition model can be performed offline, after the rewrite module 13 and the accuracy recognition model which are trained offline are deployed to a server or a vehicle, the server or the vehicle can rewrite the voice request of the current wheel by using the voice request rewrite model of the previous wheel after receiving the voice request of the current wheel, and perform accuracy recognition on the rewritten voice request of the current wheel by using the accuracy recognition model. In particular, for the simplified voice request input by the user after the voice request of the current round is obtained as the voice request of the two rounds, the voice request of the previous round after rewriting can be obtained when the voice request of the previous round is obtained. For example, in the case that the "smaller" voice request of the current round is the third round, if the first round is "the volume is large", the second round is "the smaller", and the second round is in the process of completing the last voice interaction, the second round can be rewritten into "the volume is smaller" through the rewriting model, so that, for the received voice request of the current round, the voice request of the previous round read in step 02 can be the rewritten "the volume is smaller" of the second round, so that the rewriting of the voice request of the current round can be realized according to the second round and the rewriting model.
Referring to fig. 15, step 05 includes:
051, obtaining precision discrimination probability of the precision identification result corresponding to each preset scale adjusting precision;
052, determining a preset scale adjustment precision with the precision discrimination probability larger than the probability threshold value as a target scale adjustment precision corresponding to the voice request of the current round to complete the voice interaction.
Referring to fig. 16, the interactive module 15 includes an obtaining unit 151 and a precision determining unit 152.
Step 051 may be implemented by the acquisition unit 151 and step 052 may be implemented by the precision determination unit 152. That is, the obtaining unit 151 may be configured to obtain the accuracy discrimination probabilities of the results of the accuracy identification corresponding to the respective preset scale adjustment accuracies. The precision determining unit 152 may be configured to determine a preset scale adjustment precision with the precision discrimination probability greater than the probability threshold as a target scale adjustment precision corresponding to the voice request to complete the voice interaction.
Specifically, according to the recognition result of each category of vehicle parts corresponding to the multiple preset scale adjustment accuracies, the accuracy recognition module 14 may provide an accuracy discrimination probability that each preset scale adjustment accuracy matches, and then may obtain multiple accuracy discrimination probabilities. If the probability threshold is 0.9, the precision identification result is that the precision judgment probability of the preset scale adjustment precision of the certain category of vehicle parts exceeds 0.9, and the server side determines that the preset scale adjustment precision of the category of vehicle parts is the target scale adjustment precision of the voice request of the current user. The probability threshold may be other values, and the probability threshold may be a default value, or may be set by the user according to the user's needs, which is not limited herein.
Therefore, the voice interaction method can obtain the precision discrimination probability of the precision recognition result corresponding to each preset scale adjustment precision, and the preset scale adjustment precision with the precision discrimination probability larger than the probability threshold is determined as the target scale adjustment precision corresponding to the voice request, so that the requirement of a user for accurately adjusting the scale adjustment precision of the vehicle part is recognized.
The voice interaction method comprises the following steps:
053, determining the voice request precision recognition error of the current wheel under the condition that the precision discrimination probability of each preset scale regulation precision is not more than the probability threshold.
Step 053 may be implemented by the precision determination unit 152. That is, the precision determining unit 152 may be configured to determine that the voice request precision recognition is incorrect when the precision discrimination probabilities of the preset scale adjustment precisions are not greater than the probability threshold.
For example, when the accuracy discrimination probabilities obtained according to the preset scale adjustment accuracy of each category are not greater than the probability threshold, that is, the probabilities of matching the accuracy recognition result of the user obtained according to the voice request with the preset scale adjustment accuracy of each category are lower than the probability threshold, for example, the probability threshold may be 0.9, it is determined that the voice request accuracy recognition is incorrect, for example, the voice request input by the user is "door open", because the vehicle door is not adjusted by the vehicle component adjusted by the scale, and thus, the voice request "door open" accuracy recognition is incorrect.
Therefore, under the condition that the precision judging probability of each preset scale adjustment precision is not greater than the probability threshold, the voice request precision recognition error is determined, and the voice requests related to the non-scale adjustment precision can be eliminated.
Referring to fig. 17, the present invention further provides a server 20. The server 20 comprises a processor 21 and a memory 22, the memory 22 having stored thereon a computer program 221, the computer program 221, when executed by the processor 21, implementing the voice interaction method in any of the above embodiments.
The server 20 of the invention executes 221 through the processor 21, can rewrite the voice request of the current wheel by reading the voice request of the previous wheel after receiving the voice request of the user for the preset function of the vehicle, so that the rewritten voice request can be recognized by the system to have the corresponding scale adjustment precision, and further sends a control instruction according to the precision recognition result to control the corresponding vehicle part to complete the voice interaction. The scale adjustment precision of the voice request is recognized in a mode of combining instruction rewriting and precision recognition, and the scale adjustment precision of the scale adjustment is realized by accurately recognizing the operation of simulating vehicle parts through voice interaction under the condition of realizing multiple rounds of voice requests.
Referring to fig. 18, the present invention also provides a non-volatile computer readable storage medium 30 containing a computer program 31. The computer program 31, when executed by the one or more processors 40, implements the voice interaction method of any of the embodiment clauses described above.
For example, the computer program 31, when executed by the processor 40, implements the steps of the data processing method of:
01, receiving a voice request of a current wheel, which is forwarded by a vehicle, for adjusting a preset function of the vehicle, wherein the preset function refers to a function of simulating the operation of vehicle parts for scale adjustment;
02, reading a voice request of the previous round of the preset function adjustment of the vehicle;
03, rewriting the voice request of the current round by using the voice request of the previous round;
04, performing precision recognition on the rewritten voice request of the current wheel;
and 05, finishing voice interaction according to the result of the precision recognition.
It will be appreciated that the computer program comprises computer program code. The computer program code may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable storage medium may include: any entity or device capable of carrying computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), software distribution medium, and the like.
When the computer program 31 of the computer-readable storage medium storage 30 of the present invention is executed by the processor 40, after receiving a voice request of a user for a preset function of a vehicle, the voice request of a previous round is read, the voice request of the current round is rewritten by the voice request of the previous round so that the rewritten voice request can be recognized by the system to have a corresponding scale adjustment precision, and then a control instruction is issued according to a precision recognition result to control a corresponding vehicle component to complete voice interaction. The scale adjustment precision of the voice request is recognized in a mode of combining instruction rewriting and precision recognition, and the scale adjustment precision of the scale adjustment is realized by accurately recognizing the operation of simulating vehicle parts through voice interaction under the condition of realizing multiple rounds of voice requests.
Claims (11)
1. A method of voice interaction, comprising:
receiving a voice request of a current wheel, which is forwarded by a vehicle and is adjusted by a preset function of the vehicle, wherein the preset function is a function of simulating the operation of vehicle parts to carry out scale adjustment;
reading a voice request of a previous round of adjusting a preset function of the vehicle;
rewriting the voice request of the current round by using the voice request of the previous round;
performing precision recognition on the rewritten voice request of the current wheel;
and finishing voice interaction according to the result of the precision recognition.
2. The voice interaction method according to claim 1, wherein the voice interaction method comprises:
and reversely mining the adjacent two rounds of voice requests with the occurrence frequency larger than the preset frequency to construct a regular engine.
3. The voice interaction method according to claim 2, wherein after reading the voice request for the previous round of the vehicle preset function adjustment, the voice interaction method comprises:
utilizing a regular engine to identify the scale adjustment precision corresponding to the voice request of the current round and the voice request of the previous round;
and completing voice interaction according to the recognized scale regulation precision under the condition that the recognition result of the regular engine is that the corresponding scale regulation precision is recognized.
4. The method of claim 3, wherein overwriting the voice request of the current round with the voice request of the previous round comprises:
and under the condition that the recognition result of the regular engine is that the corresponding scale adjustment precision cannot be recognized, rewriting the voice request of the current round by using the voice request of the previous round.
5. The method of claim 1, wherein overwriting the voice request of the current round with the voice request of the previous round comprises:
obtaining a rewriting model through rewriting training data training, wherein the rewriting training data comprises two adjacent rounds of voice requests;
and rewriting the voice request of the current round by using the voice request of the previous round and the rewriting model.
6. The voice interaction method of claim 1, wherein the performing precision recognition on the rewritten voice request of the current round comprises:
training precision training data to obtain a precision recognition model, wherein the precision training data is related to a vehicle part capable of being subjected to scale adjustment, a scale adjustment range of the vehicle part and a scale adjustment precision range of the part;
and performing precision recognition on the rewritten voice request of the current wheel by using the precision recognition model.
7. The voice interaction method according to claim 6, wherein the completing voice interaction according to the result of the precision recognition comprises:
obtaining precision judging probability of the precision identification result corresponding to each preset scale adjustment precision;
and determining the preset scale adjustment precision with the precision discrimination probability larger than the probability threshold as the target scale adjustment precision corresponding to the voice request so as to complete voice interaction.
8. The voice interaction method according to claim 7, wherein the voice interaction method comprises:
and under the condition that the precision judging probability of each preset scale adjusting precision is not greater than a probability threshold, determining that the precision identification of the voice request of the current wheel is wrong.
9. A voice interaction apparatus, comprising:
the system comprises a receiving instruction module, a voice processing module and a voice processing module, wherein the receiving instruction module is used for receiving a voice request of a current wheel, which is forwarded by a vehicle, for adjusting a preset function of the vehicle, and the preset function is a function for simulating the operation of vehicle parts to perform scale adjustment;
the reading instruction module is used for reading a voice request of the previous round of adjusting the preset function of the vehicle;
the rewriting module is used for rewriting the voice request of the current round by utilizing the voice request of the previous round;
the precision recognition module is used for carrying out precision recognition on the rewritten voice request of the current wheel;
and the interactive module is used for finishing voice interaction according to the precision recognition result.
10. A server, characterized in that the server comprises a processor and a memory, the memory having stored thereon a computer program which, when executed by the processor, implements the voice interaction method of any one of claims 1-8.
11. A non-transitory computer-readable storage medium embodying a computer program, wherein the computer program, when executed by one or more processors, implements the voice interaction method of any of claims 1-8.
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| CN115064170A (en) * | 2022-08-17 | 2022-09-16 | 广州小鹏汽车科技有限公司 | Voice interaction method, server and storage medium |
| CN115064170B (en) * | 2022-08-17 | 2022-12-13 | 广州小鹏汽车科技有限公司 | Voice interaction method, server and storage medium |
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