Interactive analytical instrument control system and control method integrated with voice recognition

Abstract

The invention belongs to the technical field of voice interaction control, and particularly relates to a voice recognition-fused interactive analytical instrument control system and a voice recognition-fused interactive analytical instrument control method. Voice signals acquired by a plurality of microphones on an interactive analytical instrument are utilized to carry out spectrum difference analysis, so that a main microphone and an auxiliary microphone are selected; voice signals collected by the main microphone and the auxiliary microphone are subjected to monomer recognition and fusion recognition, and an operation instruction text generated by voice fusion is compared with operation instruction texts generated by the main microphone and the auxiliary microphone to determine a final operation instruction; the accuracy, robustness and reliability of speech recognition can be remarkably improved, misrecognition is reduced, and the consistency of signal quality and operation instructions is improved, so that better user experience and interaction effect are provided.

Description

Technical Field

[0001] The present invention belongs to the technical field of voice interaction control, and in particular relates to a control system and a control method for an interactive analysis instrument integrating voice recognition. Background Art

[0002] Interactive analytical instruments are scientific instruments that have the ability to communicate with users in two directions and automatically adjust their operating behavior based on user input or instructions. Such instruments are widely used in laboratory testing (such as analytical instruments) and industrial production (such as production line equipment) in many fields such as chemistry, biology, and physics. Direct interaction with the device through voice commands, without the need for manual operation or keyboard input, significantly improves operational efficiency, especially when repetitive tasks need to be performed frequently.

[0003] In industrial and laboratory environments, interactive analysis instruments are often subject to interference from multiple noise sources, such as the running sounds of fans, pumps, and other mechanical equipment. These noise sources can significantly interfere with the acquisition of voice signals and reduce the accuracy of speech recognition. In addition, due to the large size of such equipment, the distance between the microphone and the operator may be far, resulting in severe attenuation of the voice signal during transmission. In order to overcome these problems, it is currently common to arrange multiple microphones on the equipment to locate the direction of the sound source to reduce interference in voice acquisition.

[0004] However, existing multi-microphone systems are usually only used to determine the main microphone and operate according to the voice commands collected by the main microphone. The selection of the main microphone is usually based on the principle of being closest to the sound source, but this method has some inherent limitations. On the one hand, even the microphone closest to the sound source may have poor signal quality at its location, which affects the effect of speech recognition. On the other hand, due to the lack of reference comparison of the voice signal collected by a single main microphone, it is difficult to determine whether the collected voice is missed, and there is a hidden danger of not fully reflecting all the characteristics of the voice. This may cause the voice recognition system to misrecognize in some cases, resulting in a deviation between the generated operation instruction text and the actual situation, thereby affecting the interactive effect of the system. Summary of the invention

[0005] The purpose of the present invention is to improve the deficiencies in the prior art and to provide an interactive analysis instrument control system and control method integrating speech recognition. By optimizing the way in which a multi-microphone system collects speech signals, the defects of voice control based solely on the main microphone can be minimized.

[0006] The objectives of the present invention can be achieved through the following technical solutions: The first aspect of the present invention provides an interactive analysis instrument control system integrating voice recognition, comprising the following modules: a voice signal sensing and acquisition module, which is used to perform human body sensing using an infrared sensing terminal provided on an interactive analysis instrument, and when a human body is sensed to be approaching, multiple microphones arranged on the interactive analysis instrument are activated to collect the voice signal emitted by the operator.

[0007] The microphone selection module is used to perform spectrum difference analysis on the voice signals collected by each microphone, thereby locating the location of the sound source, and selecting the main microphone and auxiliary microphone based on the sound source location.

[0008] The voice segment division module is used to divide the voice signals collected by the main microphone and the auxiliary microphone into voice segments and non-voice segments using a voice activity detection algorithm, and retain the voice segments.

[0009] The single voice recognition module is used to input the voice segments retained by the main microphone and the auxiliary microphone into the voice recognition system to generate the operation instruction text generated by the main microphone and the auxiliary microphone.

[0010] The fusion speech recognition module is used to perform frequency domain fusion on the speech segments retained by the main microphone and the auxiliary microphone, and input the fused speech into the speech recognition system to generate operation instruction text.

[0011] The operation instruction determination module is used to compare the operation instruction text generated by voice fusion with the operation instruction text generated by the main microphone and the auxiliary microphone, thereby determining the final operation instruction.

[0012] The operation execution feedback module is used to obtain the operator's feedback information during the execution of the operation by the interactive analysis instrument according to the final operation instruction. When the feedback indicates that the operation fails, the valid operation instruction text is selected from the operation instruction text previously generated by the main microphone and the auxiliary microphone for secondary operation.

[0013] The second aspect of the present invention proposes a method for controlling an interactive analysis instrument integrating voice recognition, comprising the following steps: S1, using an infrared sensing terminal provided on the interactive analysis instrument to sense the human body, and when a human body is sensed to be approaching, starting multiple microphones arranged on the interactive analysis instrument to collect the voice signal emitted by the operator.

[0014] S2. Perform spectrum difference analysis on the voice signals collected by each microphone to locate the location of the sound source, and select the main microphone and the auxiliary microphone with the sound source location as the center.

[0015] S3. Divide the voice signals collected by the main microphone and the auxiliary microphone into voice segments and non-voice segments using a voice activity detection algorithm, and retain the voice segments.

[0016] S4. Input the voice segments retained by the main microphone and the auxiliary microphone into a voice recognition system to generate operation instruction texts generated by the main microphone and the auxiliary microphone.

[0017] S5. Perform frequency domain fusion on the voice segments retained by the main microphone and the auxiliary microphone, and input the fused voice into a voice recognition system to generate an operation instruction text.

[0018] S6. Compare the operation instruction text generated by voice fusion with the operation instruction text generated by the main microphone and the auxiliary microphone, thereby determining the final operation instruction.

[0019] S7. Obtaining feedback information from the operator while the interactive analysis instrument is performing operations according to the final operation instructions.

[0020] S8. When the feedback indicates that the operation fails, a valid operation instruction text is selected from the operation instruction text previously generated by the main microphone and the auxiliary microphone to perform a secondary operation.

[0021] In combination with all the above-mentioned technical solutions, the positive effects of the present invention are as follows: 1. The present invention performs spectrum difference analysis on the voice signals collected by multiple microphones on the interactive analysis instrument, thereby selecting the main microphone and the auxiliary microphone, and then performs monomer recognition and fusion recognition on the voice signals collected by the main microphone and the auxiliary microphone, so as to determine the operation instructions by combining the recognition results of the two, which can significantly improve the accuracy, robustness and reliability of voice recognition, reduce misrecognition, improve signal quality and consistency of operation instructions, and thus provide better user experience and interactive effects.

[0022] 2. The present invention obtains feedback information from the operator during the execution of the operation by the interactive analysis instrument according to the determined operation instruction. When the feedback indicates that the operation fails, the effective operation instruction text is selected from the operation instruction text previously generated by the main microphone and the auxiliary microphone for secondary operation. In this way, by obtaining the operator's immediate feedback, the operation failure can be quickly discovered, and measures can be taken to correct it, avoiding chain errors in subsequent operations caused by a failed operation instruction. At the same time, after the first operation fails, the secondary operation can be quickly performed, reducing the time of operation interruption and improving the continuity and stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0024] Figure 1 It is a schematic diagram of system module connection of the present invention.

[0025] Figure 2 This is a schematic diagram of voice signal sensing and collection in the present invention.

[0026] Figure 3 It is a diagram of the steps for implementing the method of the present invention. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] Example 1

[0029] The present invention proposes an interactive analysis instrument control system integrating speech recognition, comprising a speech signal sensing and acquisition module, a microphone selection module, a speech segment division module, a single speech recognition module, a fusion speech recognition module, an operation instruction determination module and an operation execution feedback module, wherein the speech signal sensing and acquisition module is connected to the microphone selection module, the microphone selection module is connected to the speech segment division module, the speech segment division module is respectively connected to the single speech recognition module and the fusion speech recognition module, the single speech recognition module and the fusion speech recognition module are both connected to the operation instruction determination module, and the operation instruction determination module and the single speech recognition module are both connected to the operation execution feedback module.

[0030] The voice signal sensing and collecting module is used to use the infrared sensing terminal arranged on the interactive analysis instrument to sense the human body. When sensing that a human body is approaching, the multiple microphones arranged on the interactive analysis instrument are activated to collect the voice signal emitted by the operator.

[0031] For the above-mentioned mid-infrared sensing terminal to trigger the multi-microphone startup, see Figure 2 shown.

[0032] In the operation of the above solution, the infrared sensing terminal may be an infrared sensor, exemplarily a pyroelectric infrared sensor, which mainly senses the presence of a human body by detecting infrared rays radiated by the human body.

[0033] It should be understood that the purpose of setting up an infrared sensing terminal on an interactive analytical instrument is to detect the presence of a human body. When a human body approaches the instrument, the sensor will trigger the start of the microphone. This ensures that the microphone is ready to receive voice commands when the operator is ready to interact with the instrument. In addition, the infrared sensing terminal only starts the microphone system when someone approaches, which can save power resources and extend the service life of the equipment.

[0034] The microphone selection module is used to perform spectrum difference analysis on the voice signals collected by each microphone, thereby locating the location of the sound source, and selecting the main microphone and the auxiliary microphone with the sound source location as the center.

[0035] As a preferred implementation of the above scheme, the speech signals collected by each microphone are subjected to spectral difference analysis as follows: the speech signals collected by each microphone are converted into the frequency domain to obtain a spectrum diagram, wherein the spectrum diagram of the speech signal is constructed with frequency as the horizontal axis and amplitude (or energy) as the vertical axis. The spectrum diagram shows the energy distribution of the speech signal at different frequencies, and the position of the energy peak is marked from the spectrum diagram, and then the frequency component corresponding to the energy peak is extracted as the main frequency component of the speech signal collected by each microphone.

[0036] It is important to know that the peak position in the spectrogram indicates that the speech signal has a stronger component at a certain frequency, that is, the main frequency component.

[0037] The main frequency components of the speech signals collected by each microphone are overlapped, and the overlapping main frequency components are extracted.

[0038] The phase value of each frequency point is extracted from the spectrum diagram of the speech signal collected by each microphone within the overlapping main frequency components.

[0039] In the optimization implementation of the above scheme, the overlapping main frequency components need to be divided into frequency points before the phase value of each frequency point is extracted. The specific division can be determined according to the sampling frequency of the spectrum and the window size to determine the frequency division interval, where the sampling frequency refers to the number of times the signal samples are collected per second, and the frequency resolution refers to the minimum frequency interval that the spectrum can distinguish. The frequency resolution depends on the sampling frequency of the spectrum and the length of the window. Specifically, Where FR is the frequency resolution, f s Indicates the sampling frequency, N indicates the number of sample points in the spectrum analysis window, and the frequency division interval is usually the frequency resolution. For example, 1024 sample points are used for spectrum analysis, and the sampling frequency is 8000 Hz, then the frequency division interval is That is, the interval between each frequency point is about 7.81Hz.

[0040] The microphones arranged on the interactive analysis instrument are numbered, and the microphones are grouped in pairs according to the numbered sequence to obtain a plurality of microphone groups.

[0041] In the example of the above scheme, assuming that there are 4 microphones arranged on the interactive analysis instrument, the numbering of the microphones can be based on a certain point on the interactive analysis instrument as a reference point, such as the center of the instrument, and the microphones are numbered in order from near to far according to the distance between the arrangement position of each microphone and the reference point. According to the numbering order, the microphones are grouped in pairs to obtain several microphone groups. The specific grouping method is Team 1: Microphone 1 and Microphone 2, Team 2: Microphone 1 and Microphone 3, Team 3: Microphone 1 and Microphone 4, Team 4: Microphone 2 and Microphone 3, Team 5: Microphone 2 and Microphone 4, Team 6: Microphone 3 and Microphone 4.

[0042] The phase values ​​of two microphones in each microphone group at each frequency point are subtracted to form a phase difference set of each microphone group, and the phase differences in the set are averaged to obtain an average phase difference corresponding to each microphone group.

[0043] It should be explained that different microphones will have different phase values ​​for the same frequency point, and the phase difference between the two microphones at the same frequency point can be reflected by calculating the phase difference.

[0044] It is further explained that the phase difference of a single frequency point may not be accurate enough due to the influence of factors such as noise. Calculating the average of the phase differences of multiple frequency points can reduce the impact of the error to a certain extent.

[0045] As another preferred implementation of the above solution, the location of the sound source is located as follows: a three-dimensional coordinate system is constructed on the interactive analysis instrument, thereby obtaining the coordinates of the layout positions of each microphone.

[0046] Specifically, the process of constructing the three-dimensional coordinate system is as follows: first, select a fixed reference point as the origin o of the three-dimensional coordinate system. This reference point can be the center point of the instrument. Next, define the three coordinate axes x, y and z of the three-dimensional coordinate system, where the x-axis is defined as the horizontal direction, which can be along the length direction of the instrument, the y-axis is defined as the width direction, perpendicular to the x-axis, and the z-axis is defined as the height direction, perpendicular to the x-axis and y-axis.

[0047] More specifically, for each microphone, the horizontal distance of the microphone along the x-axis direction is measured, that is, the distance from the reference point o to the horizontal projection point of the microphone, the horizontal distance of the microphone along the y-axis direction is measured, that is, the distance from the reference point o to the horizontal projection point of the microphone, and the height of the microphone is measured, that is, the height distance from the reference point o to the microphone, thereby recording the measured x, y and z coordinates to form the position coordinates of the microphone.

[0048] The present invention determines the position of the microphone by defining a three-dimensional coordinate system, which can significantly improve the accuracy and robustness of the system, improve the user experience, and provide a solid foundation for subsequent sound source positioning. This coordinate system definition method has wide applicability and superiority in practical applications due to its advantages such as clear direction definition, convenient measurement and recording, and improved positioning accuracy.

[0049] The average phase difference of each microphone group is converted into the time difference of the sound wave arriving at the two microphones.

[0050] It should be noted that the present invention uses a positioning technology based on time difference to locate the sound source. This method uses the time difference of sound waves propagating between different receiving points (such as microphones) to determine the position of the sound source.

[0051] In the above specific embodiment of the time difference, assuming that the position coordinates of a microphone group are (x1, y1, z1) and (x2, y2, z2), the average phase difference is Then the time difference of the sound wave reaching the microphone group is Where f is the frequency of the sound wave signal and π represents pi.

[0052] The time difference of the sound waves reaching the two microphones in each microphone group is combined with the propagation speed of the sound waves to calculate the sound wave propagation distance difference corresponding to each microphone group.

[0053] In the implementation of the above scheme, since the propagation speed c of the sound wave is known (approximately 342m / s under standard atmospheric pressure), the time difference between the sound waves reaching the two microphones is combined with the sound wave propagation speed c to calculate the sound wave propagation distance difference, and the specific calculation formula is Δd=c*Δt.

[0054] A hypersphere equation is established based on the coordinates of the locations of the two microphones in each microphone group and the difference in sound wave propagation distances.

[0055] It should be noted that when the difference in sound wave propagation distance of a microphone group is known, the sound source must be located on a hypersphere with the line connecting the two microphones as the diameter. For example, the equation of the hypersphere is (x-x1) 2 +(y-y1) 2 +(z-z1) 2 =(x-x2) 2 +(y-y2) 2 +(z-z2) 2 +(Δd) 2 If more microphone groups are added, multiple such hyperspheres can be obtained, and the intersection of these hyperspheres is the location of the sound source.

[0056] The hypersphere equations established by each microphone group are formed into a nonlinear equation group, and then the position coordinates of the sound source are obtained by solving the equation group through numerical calculation.

[0057] The least squares method can be used to solve the above system of equations.

[0058] As a further preferred implementation of the above scheme, the main microphone is selected with the sound source position as the center, referring to the following process: the position coordinates of the sound source are compared with the position coordinates of each microphone to obtain the distance between the sound source and each microphone, and the microphone with the shortest distance is extracted as the main microphone.

[0059] As a further preferred implementation of the above scheme, the auxiliary microphone refers to the following selection process: a sphere is drawn with the position coordinates of the sound source as the center and the limited distance as the radius, the area inside the sphere is the selection range, and then the microphones other than the main microphone within the selection range are selected as alternative microphones.

[0060] In particular, multiple factors need to be considered when determining the limited distance, including the spacing between the microphones, the expected position of the sound source, etc., and 3 meters (laboratory environment) or 5 meters (industrial environment) is usually recommended.

[0061] The signal-to-noise ratio of the candidate microphone is calculated based on the spectrum of the speech signal collected by the candidate microphone. The specific operation is as follows: the collected speech signal is divided into speech segments and non-speech segments using a speech activity detection algorithm. The non-speech segment is the noise segment.

[0062] The main frequency components of the speech segment and their corresponding energy distribution characteristics are extracted from the spectrum diagram, and the signal energy of the speech segment is calculated accordingly. The signal energy of the speech segment can be calculated using a short-time energy calculation method, that is, summing the square of the signal amplitude within the speech segment.

[0063] The main frequency distribution of the non-speech segment and its corresponding energy distribution characteristics are extracted from the spectrum graph. Similarly, the signal energy of the non-speech segment is calculated with reference to the signal energy of the speech segment.

[0064] The signal energy of all speech segments and the signal energy of all non-speech segments in the speech signal collected by the candidate microphone are averaged, and the calculation result is used to calculate the signal-to-noise ratio of the speech signal collected by the candidate microphone. The specific signal-to-noise ratio calculation formula is: In the formula They represent the average signal energy of speech segments and the average signal energy of non-speech segments respectively.

[0065] The signal-to-noise ratio of the candidate microphone is compared with a preset critical signal-to-noise ratio, wherein the purpose of setting the critical signal-to-noise ratio is to provide assistance for the screening of auxiliary microphones with good signal quality, and to extract the candidate microphone that reaches the critical signal-to-noise ratio as the auxiliary microphone.

[0066] The present invention selects auxiliary microphones with good signal-to-noise ratios according to the signal-to-noise ratios of collected voice signals within a selection range formed by a sphere with the position coordinates of the sound source as the center and a limited distance as the radius, rather than directly selecting microphones that fall within the selection range, which can significantly improve the reliability of voice signal collection. This method can better cope with the challenges of voice recognition in complex environments, improve the quality of voice signals, and reduce the risk of misrecognition.

[0067] The speech segment division module is used to divide the speech signals collected by the main microphone and the auxiliary microphone into speech segments and non-speech segments by using a speech activity detection algorithm, and retain the speech segments.

[0068] The single voice recognition module is used to input the voice segments retained by the main microphone and the auxiliary microphone into the voice recognition system to generate the operation instruction text generated by the main microphone and the auxiliary microphone.

[0069] The fusion speech recognition module is used to perform frequency domain fusion on the speech segments retained by the main microphone and the auxiliary microphone, and input the fused speech into the speech recognition system to generate an operation instruction text.

[0070] In one achievable method, the speech segments retained by the main microphone and the auxiliary microphone are fused in the frequency domain as follows: the frequency region where the speech segment is located is located in the spectrum diagram corresponding to the main microphone as the main frequency region, and the frequency region is divided into frequency points, and the frequency value of each frequency point is extracted as the main spectrum value.

[0071] The main frequency area is located in the spectrum diagram corresponding to the auxiliary microphone, and the spectrum value of each frequency point is extracted as the auxiliary frequency value.

[0072] The main spectrum value and the auxiliary spectrum value of each frequency point are fused to obtain the fused frequency domain signal.

[0073] The fused frequency domain signal is converted back to the time domain signal through inverse fast Fourier transform to obtain the fused speech signal.

[0074] It should be understood that spectrally fusing the voice signals collected by the main microphone and the auxiliary microphone can enhance the signal. The fused voice signal has a higher signal-to-noise ratio, better voice quality and lower risk of misrecognition.

[0075] The operation instruction determination module is used to compare the operation instruction text generated by voice fusion with the operation instruction text generated by the main microphone and the auxiliary microphone, thereby determining the final operation instruction. The specific operation is as follows: the operation instruction text generated by voice fusion and the operation instruction text generated by the main microphone and the auxiliary microphone are respectively segmented to obtain a number of segmented words for each operation instruction text, and the segmented words are numbered according to their positions in the operation instruction text.

[0076] In the example of the above solution, the operation instruction text generated by voice fusion is "turn on the device and increase the temperature", the operation instruction text generated by the main microphone is "turn on the device and increase the humidity", and the operation instruction text generated by the auxiliary microphone is "turn on the device and reduce the pressure". In this example, the word segmentation result of the voice fusion operation instruction text is ["turn on", "device", "and", "increase", "temperature"], the word segmentation result of the operation instruction text generated by the main microphone is ["turn on", "device", "and", "increase", "humidity"], and the word segmentation result of the operation instruction text generated by the auxiliary microphone is ["turn on", "device", "and", "reduce", "pressure"], and the divided words are numbered 1, 2, 3, 4, and 5 in order of precedence.

[0077] The segmented words with the same number in the speech fusion operation instruction text, the main microphone operation instruction text, and the auxiliary microphone operation instruction text are extracted in sequence for comparison. If the segmented words in multiple texts are consistent, the consistent segmented words are selected as the final content. If they are inconsistent, the segmented word with the largest number of occurrences is selected as the final content. If the number of occurrences is evenly distributed, the segmented word in the speech fusion operation instruction text is selected as the final content.

[0078] In a further example, the first segmentation word "turn on" in the voice fusion operation instruction text, the main microphone operation instruction text, and the auxiliary microphone operation instruction text are consistent, and the final content is "turn on"; the second segmentation word "equipment" is consistent, and the final content is "equipment"; the third segmentation word "and" is consistent, and the final content is "and"; the fourth segmentation word "increase" and "lower" are inconsistent, but increase appears the most times, so the final content is "increase"; the fifth segmentation word "temperature", "humidity" and "pressure" are inconsistent and the number of occurrences is evenly distributed, so the final content is "temperature".

[0079] The final contents of each participle are combined into a final operation instruction. In the above example, the final operation instruction is "turn on the device and increase the temperature".

[0080] It needs to be explained that when the word segmentation in different operation instruction texts is inconsistent, the word segmentation content in the voice fusion operation instruction text is preferentially used because the risk of misrecognition of the fused voice signal is more advantageous than that of the main microphone operation instruction text and the auxiliary microphone operation instruction text.

[0081] The operation execution feedback module is used to obtain operator feedback information during the interactive analysis instrument executing the operation according to the final operation instruction. When the feedback indicates that the operation fails, the valid operation instruction text is selected from the operation instruction text previously generated by the main microphone and the auxiliary microphone for secondary operation.

[0082] In the innovative implementation of the above scheme, the selected valid operation instruction text is: the operation instruction text generated by the main microphone is selected as the valid operation instruction text from the operation instruction texts previously generated by the main microphone and the auxiliary microphone.

[0083] It should be understood that the reason why the operation instruction text generated by the main microphone is used as the effective operation instruction text is that the main microphone is usually closest to the sound source, so its signal quality is usually good and its reliability is high. When the operation fails, directly using the operation instruction text generated by the main microphone for secondary operation can simplify the processing logic and reduce the complexity of the algorithm.

[0084] Example 2

[0085] Reference Figure 3 As shown, the present invention proposes a control method for an interactive analysis instrument integrating voice recognition, comprising the following steps: S1, utilizing an infrared sensing terminal provided on the interactive analysis instrument to sense a human body, and when sensing that a human body is approaching, activating multiple microphones arranged on the interactive analysis instrument to collect a voice signal emitted by the operator.

[0086] S2. Perform spectrum difference analysis on the voice signals collected by each microphone to locate the location of the sound source, and select the main microphone and the auxiliary microphone with the sound source location as the center.

[0087] S3. Divide the voice signals collected by the main microphone and the auxiliary microphone into voice segments and non-voice segments using a voice activity detection algorithm, and retain the voice segments.

[0088] S4. Input the voice segments retained by the main microphone and the auxiliary microphone into a voice recognition system to generate operation instruction texts generated by the main microphone and the auxiliary microphone.

[0089] S5. Perform frequency domain fusion on the voice segments retained by the main microphone and the auxiliary microphone, and input the fused voice into a voice recognition system to generate an operation instruction text.

[0090] S6. Compare the operation instruction text generated by voice fusion with the operation instruction text generated by the main microphone and the auxiliary microphone, thereby determining the final operation instruction.

[0091] S7. Acquire feedback information from the operator while the interactive analysis instrument is performing the operation according to the final operation instruction.

[0092] S8. If the feedback indicates that the operation fails, a valid operation instruction text is selected from the operation instruction texts previously generated by the main microphone and the auxiliary microphone to perform a secondary operation.

[0093] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. An interactive analysis instrument control system integrating speech recognition, characterized in that: Includes the following modules: The voice signal sensing and acquisition module is used to use the infrared sensing terminal provided on the interactive analysis instrument to sense the human body, and when a human body is sensed to be approaching, the multiple microphones arranged on the interactive analysis instrument are activated to collect the voice signal emitted by the operator; The microphone selection module is used to perform spectrum difference analysis on the voice signals collected by each microphone, thereby locating the location of the sound source, and selecting the main microphone and auxiliary microphone based on the sound source location; A speech segment division module is used to divide the speech signals collected by the main microphone and the auxiliary microphone into speech segments and non-speech segments using a speech activity detection algorithm, and retain the speech segments; A single voice recognition module is used to input the voice segments retained by the main microphone and the auxiliary microphone into the voice recognition system to generate the operation instruction text generated by the main microphone and the auxiliary microphone; A fusion speech recognition module is used to perform frequency domain fusion on the speech segments retained by the main microphone and the auxiliary microphone, and input the fused speech into the speech recognition system to generate an operation instruction text; An operation instruction determination module is used to compare the operation instruction text generated by voice fusion with the operation instruction text generated by the main microphone and the auxiliary microphone, thereby determining the final operation instruction; The operation execution feedback module is used to obtain the operator's feedback information during the execution of the operation by the interactive analysis instrument according to the final operation instruction. When the feedback indicates that the operation fails, the valid operation instruction text is selected from the operation instruction text previously generated by the main microphone and the auxiliary microphone for secondary operation.

2. The interactive analysis instrument control system integrating speech recognition according to claim 1, characterized in that: The process of analyzing the difference in the frequency spectrum of the speech signals collected by each microphone is as follows: The speech signals collected by each microphone are converted into the frequency domain to obtain a spectrogram, and the positions of the energy peaks are marked from the spectrogram, and then the frequency components corresponding to the energy peaks are extracted as the main frequency components of the speech signals collected by each microphone; Overlap the main frequency components of the speech signals collected by each microphone, and extract the overlapping main frequency components; Extracting the phase value of each frequency point from the spectrum diagram of the speech signal collected by each microphone within the overlapping main frequency components; The microphones arranged on the interactive analysis instrument are numbered, and the microphones are grouped in pairs according to the numbering sequence to obtain a plurality of microphone groups; The phase values ​​of two microphones in each microphone group at each frequency point are subtracted to form a phase difference set of each microphone group, and the phase differences in the set are averaged to obtain an average phase difference corresponding to each microphone group.

3. The interactive analysis instrument control system integrating speech recognition as claimed in claim 2, characterized in that: The location of the localization sound source is implemented as follows: A three-dimensional coordinate system is constructed on the interactive analysis instrument, thereby obtaining the coordinates of the layout positions of each microphone; Convert the average phase difference of each microphone group into the time difference of the sound wave arriving at the two microphones; The time difference between the sound waves in each microphone group reaching the two microphones is combined with the propagation speed of the sound waves to calculate the sound wave propagation distance difference corresponding to each microphone group; A hypersphere equation is established based on the coordinates of the locations of the two microphones in each microphone group and the difference in sound wave propagation distances; The hypersphere equations established by each microphone group are formed into a nonlinear equation group, and then the position coordinates of the sound source are obtained by solving the equation group through numerical calculation.

4. The interactive analysis instrument control system integrating speech recognition as claimed in claim 3, characterized in that: The process of selecting the main microphone based on the sound source position is as follows: The position coordinates of the sound source are compared with the position coordinates of each microphone to obtain the distance between the sound source and each microphone, and the microphone with the shortest distance is extracted as the main microphone.

5. The interactive analysis instrument control system integrating speech recognition as claimed in claim 3, characterized in that: The auxiliary microphone is selected as follows: A sphere is constructed with the position coordinates of the sound source as the center and the limited distance as the radius. The area inside the sphere is the selection range, and the microphones other than the main microphone within the selection range are selected as candidate microphones. Calculate the signal-to-noise ratio of the candidate microphone based on the spectrum of the speech signal collected by the candidate microphone; The signal-to-noise ratio of the candidate microphones is compared with a preset critical signal-to-noise ratio, and the candidate microphones reaching the critical signal-to-noise ratio are extracted as auxiliary microphones.

6. The interactive analysis instrument control system integrating speech recognition as claimed in claim 5, characterized in that: The calculation of the signal-to-noise ratio of the candidate microphone based on the spectrum of the speech signal collected by the candidate microphone is implemented as follows: Based on the divided speech segments and non-speech segments, the speech segments and non-speech segments are marked in the spectrum graph of the candidate microphone, and then the energy distribution characteristics of the speech segments and non-speech segments are respectively extracted; The signal energy of the speech segment is calculated based on the energy distribution characteristics of the speech segment, and the signal energy of the non-speech segment is calculated similarly; The signal energies of all speech segments and the signal energies of all non-speech segments corresponding to each candidate microphone are averaged to thereby calculate the signal-to-noise ratio of the candidate microphone.

7. The interactive analysis instrument control system integrating speech recognition as claimed in claim 1, characterized in that: The frequency domain fusion of the voice segments retained by the main microphone and the auxiliary microphone is performed as follows: Locate the frequency region where the speech segment is located in the spectrum graph corresponding to the subject microphone as the subject frequency region, divide the frequency region into frequency points, and then extract the frequency value of each frequency point as the subject spectrum value; Locate the main frequency area in the spectrum diagram corresponding to the auxiliary microphone, and similarly extract the auxiliary spectrum value of each frequency point; The main spectrum value and the auxiliary spectrum value of each frequency point are fused to obtain a fused frequency domain signal; The fused frequency domain signal is converted back to the time domain signal through inverse fast Fourier transform to obtain the fused speech signal.

8. The interactive analysis instrument control system integrating speech recognition as claimed in claim 1, characterized in that: The process of determining the final operation instruction is as follows: The operation instruction text generated by the speech fusion and the operation instruction text generated by the main microphone and the auxiliary microphone are segmented to obtain a number of segmented words for each operation instruction text, and the segmented words are numbered according to their positions in the operation instruction text; Extract the segmented words with the same number in the speech fusion operation instruction text, the main microphone operation instruction text, and the auxiliary microphone operation instruction text in order of number and compare them. If the segmented words in multiple texts are consistent, select the consistent segmented words as the final content. If they are inconsistent, select the segmented words with the largest number of occurrences as the final content. If the number of occurrences is evenly distributed, select the segmented words in the speech fusion operation instruction text as the final content. Combine the final contents of each word segment into the final operation instruction.

9. The interactive analysis instrument control system integrating speech recognition as claimed in claim 1, characterized in that: The process of selecting the valid operation instruction text is as follows: The operation instruction text generated by the main microphone is selected from the operation instruction texts previously generated by the main microphone and the auxiliary microphone as the valid operation instruction text.

10. A method for controlling an interactive analysis instrument integrating speech recognition, characterized in that: The following steps are involved: S1. Use the infrared sensing terminal provided on the interactive analysis instrument to sense the human body. When sensing that a human body is approaching, activate the multiple microphones arranged on the interactive analysis instrument to collect the voice signal emitted by the operator. S2, performing spectrum difference analysis on the voice signals collected by each microphone, thereby locating the location of the sound source, and selecting the main microphone and the auxiliary microphone with the sound source location as the center; S3, dividing the voice signals collected by the main microphone and the auxiliary microphone into voice segments and non-voice segments using a voice activity detection algorithm, and retaining the voice segments; S4, inputting the voice segments retained by the main microphone and the auxiliary microphone into the voice recognition system to generate operation instruction texts generated by the main microphone and the auxiliary microphone; S5, performing frequency domain fusion on the voice segments retained by the main microphone and the auxiliary microphone, and inputting the fused voice into a voice recognition system to generate an operation instruction text; S6, comparing the operation instruction text generated by voice fusion with the operation instruction text generated by the main microphone and the auxiliary microphone, thereby determining the final operation instruction; S7, obtaining feedback information from the operator during the period when the interactive analysis instrument performs the operation according to the final operation instruction; S8. When the feedback indicates that the operation fails, a valid operation instruction text is selected from the operation instruction text previously generated by the main microphone and the auxiliary microphone to perform a secondary operation.

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