EVALUATION METHOD AND DEVICE OF AIR TRAFFIC CONTROL SPEECH RECOGNITION SYSTEM

Abstract

An evaluation method and system of an air traffic control (ATC) speech recognition system are provided, the method includes: S1, obtaining, grading and inputting ATC speech data; S2, constructing an evaluation index system; S3, determining weights of utility layer indexes and support layer indexes under different levels ATC speech data; S4, calculating a utility layer score and a support layer score, and adding the scores of the utility layer and the support layer to obtain comprehensive scores of the utility layer and the support layer; S5, determining, weights of the utility layer and the support layer; S6, adding a product of the utility layer weight with the utility layer comprehensive score and a product of the support layer weight with the support layer comprehensive score to obtain a comprehensive score of the speech recognition system; and S7, determining a level of the recognition performance of the speech recognition system.

Description

TECHNICAL FIELD

The disclosure relates to the field of speech recognition, and more particularly to an evaluation method and system of an air traffic control (ATC) speech recognition system.

BACKGROUND

In recent years, with a continuous development of an aviation industry, a number of flights is also increasing year by year, which poses a more severe challenge to keep air traffic smooth and ensure flight safety. Communication and coordination between air traffic controllers and the controller sending a control instruction to crew are main means to adjust flight conflict, promote smooth air traffic, and ensure flight safety. Therefore, it is of great significance to correctly understand air traffic control (ATC) speech to ensure flight safety.

A speech recognition system can convert speech information to text information, thus increasing an information acquisition dimension to prevent an incorrect understanding for information between the controllers and between the controller and the crew, relieving workload of the controller, and ensuring the flight safety. An ATC instruction is a language with professional characteristics, main features of the ATC instruction include fast speech speed, concise and fixed instruction structure and special pronunciation. In the current stage, a quality of the speech recognition system is uneven, different speech recognition systems consider different emphasizes, such as response time, accuracy and other indexes, which makes performance and a recognition effect of the speech recognition system have a big difference, and there is a lack of a method for evaluating the quality of the speech recognition system to quantitatively evaluate the speech recognition system uniformly.

In a field of speech recognition, current evaluation methods are mainly aimed at voice quality and recognition algorithm, an evaluation method for the speech recognition system has not proposed yet. Based on a particularity of an ATC field, it is necessary to propose an evaluation method of an ATC speech recognition system in the ATC field based on traditional system evaluation methods, to overcome a problem of unified quantitative evaluation of the speech recognition systems in the ATC field.

SUMMARY

A purpose of the disclosure is to overcome a problem of unified quantitative evaluation of a speech recognition system in the related art, and provide an evaluation method and system of an air traffic control (ATC) speech recognition system.

In order to achieve the above purpose, the disclosure provides the following technical solutions.

An evaluation method of an ATC speech recognition system is provided, and the evaluation method includes:

• • S1, obtaining ATC speech data; grading, based on a quality level of the ATC speech data, the ATC speech data to obtain speech data with different quality levels, and inputting the speech data with different quality levels into a to-be-evaluated ATC speech recognition system;
  • S2, constructing an evaluation index system; where indexes of the evaluation index system include utility layer indexes and support layer indexes;
  • S3, determining, by using an improved group-G2 method and an improved criteria importance through intercriteria correlation (CRITIC) method, weights of the utility layer indexes and weights of the support layer indexes under the ATC speech data with different quality levels;
  • S4, calculating, according to the weights of the utility layer indexes and the weights of the support layer indexes under the ATC speech data with different quality levels, scores of a utility layer and scores of a support layer under the ATC speech data with different quality levels by using an improved technique for order preference by similarity to ideal solution (TOPSIS) method; and adding the scores of the utility layer to obtain a comprehensive score of the utility layer and adding the scores of the support layer to obtain a comprehensive score of the support layer;
    • where the TOPSIS method is to determine Z⁺ and Z⁻ of an index through a statistical method, to thereby score a recognition performance of the to-be-evaluated ATC speech recognition system; and
    • where Z⁺ represents a set containing optical values of evaluation indexes of the to-be-evaluated ATC speech recognition system, and Z⁻ represents a set containing worst values of the evaluation indexes of the to-be-evaluated ATC speech recognition system;
  • S5, determining, according to the comprehensive score of the utility layer and the comprehensive score of the support layer, a weight of the utility layer and a weight of the support layer of the to-be-evaluated ATC speech recognition system by using the improved group-G2 method and the improved CRITIC method;
  • S6, multiplying the weight of the utility layer and the comprehensive score of the utility layer to obtain a first product, multiplying the weight of the support layer and the comprehensive score of the support layer to obtain a second product, and adding the first product and the second product to obtain a comprehensive score of the to-be-evaluated ATC speech recognition system; and S7, determining, according to the comprehensive score of the to-be-evaluated ATC speech recognition system, a level of the recognition performance of the to-be-evaluated ATC speech recognition system.

In an exemplary embodiment, the evaluation method of the ATC speech recognition system further includes: comparing the level of the recognition performance of the to-be-evaluated ATC speech recognition system with levels of the recognition performance of other ATC speech recognition systems individually, thereby to obtain a target ATC speech recognition system, and applying the target ATC speech recognition system to recognize an ATC instruction, to thereby make an air traffic controller or a crew member correctly understand the ATC instruction.

In an embodiment, in step S2, the utility layer indexes include accuracy indexes and a recognition rate index, the recognition rate index includes a processing time consumption, and the processing time consumption refers to a time consumed from a moment when the to-be-evaluated ATC speech recognition system receives completely inputted ATC speech data to a moment when the to-be-evaluated ATC speech recognition system recognizes a result and outputs the result.

In an embodiment, the accuracy indexes include a sentence accuracy, a word accuracy and a keyword accuracy, and a calculation expression of the keyword accuracy is expressed as follows:

$K_{\mathrm{acc}}=1-\left(\frac{K_{\mathrm{error}}}{K_{\mathrm{total}}}\times 100\%\right);$

where K_acc represents the keyword accuracy, K_error represents a number of keyword errors in a sentence, and K_total represents a total number of keywords in a sentence.

In an embodiment, in step S2, the support layer indexes include reliability indexes and maintainability indexes.

In an embodiment, in step S4, calculation formulas of Z⁺ and Z⁻ of the index in the improved TOPSIS method are as follows:

$Z^{+}=\left(\max \left\{z_{11},z_{21},\dots ,z_{n1}\right\},\max \left\{z_{12},z_{22},\dots ,z_{n2}\right\},\dots \max \left\{z_{1n},z_{2n},\dots ,z_{\mathrm{nn}}\right\}\right);$ $Z^{-}=\left(\min \left\{z_{11},z_{21},\dots ,z_{n1}\right\},\min \left\{z_{12},z_{22},\dots ,z_{n2}\right\},\dots \min \left\{z_{1n},z_{2n},\dots ,z_{\mathrm{nn}}\right\}\right);$

where z₁₁ represents a first index value of a first system, z₂₁ represents a first index value of a second system, z₂₂ represents a second index value of a second system, z₂₂ represents a n-th index value of the second system, and z_nn represents a n-th index value of a n-th system.

In an embodiment, in step S7, the determining, according to the comprehensive score of the to-be-evaluated ATC speech recognition system, a level of the recognition performance of the to-be-evaluated ATC speech recognition system includes: determining, according to the comprehensive score of the to-be-evaluated ATC speech recognition system and a determination principle, the level of the recognition performance of the to-be-evaluated ATC speech recognition system; and the determination principle includes:

• • determining, in response to the comprehensive score being in a range of 0.0-0.3 scores, containing 0.3 scores, the level of the recognition performance to be level 1;
  • determining, in response to the comprehensive score being in a range of 0.3-0.5 scores, containing 0.5 scores, the level of the recognition performance to be level 2;
  • determining, in response to the comprehensive score being in a range of 0.5-0.7 scores, containing 0.7 scores, the level of the recognition performance to be level 3;
  • determining, in response to the comprehensive score being in a range of 0.7-0.9 scores, containing 0.9 scores, the level of the recognition performance to be level 4; and
  • determining, in response to the comprehensive score being in a range of 0.9-1.0 scores, containing 1.0 score, the level of the recognition performance to be level 5.

An evaluation system of the ATC speech recognition system is provided, the evaluation system includes at least one processor and a memory communicatively connected to the at least one processor, the memory stores an instruction executed by the at least one processor, and the instruction is configured to be executed by the at least one processor, to make the at least one processor to execute any one steps of the evaluation method.

Compared to the related art, beneficial effects of the disclosure are as follows.

• • 1. The improved TOPSIS method overcomes the problem of unified quantitative evaluation of the speech recognition system in an ATC field, and achieves comprehensive quantification evaluations of single and multiple indexes for the ATC speech recognition system.
  • 2. A method fusing the improved group-G2 method and the improved CRITIC method of the disclosure is used to determine a weight of each index, the method is a specific implementation of subjective weighting combined with objective weighting to ultimately determine the weights of the indexes, and the method overcomes one-sidedness caused by subjective weighting and a deviation caused by objective weighting, and makes an evaluation effect more reliable.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 illustrates a flowchart of an evaluation method of an air traffic control (ATC) speech recognition system according to an embodiment of the disclosure.

FIG. 2 illustrates a tree diagram of indexes of the evaluation method of the ATC speech recognition system according to an embodiment of the disclosure.

FIG. 3 illustrates a schematic diagram of working times of the ATC speech recognition system according to an embodiment of the disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS

The disclosure will be described in detail below in conjunction with embodiments and specific implementations. However, it cannot be understood that a scope of the above topics of the disclosure is merely limited to the following embodiments, and all technologies implemented based on a content of the disclosure fall within a scope of the disclosure.

Embodiment

As shown in FIG. 1, the embodiment includes the following steps S1-S7.

In step S1, air traffic control (ATC) speech data is obtained, the ATC speech data is graded to obtain ATC speech data with different quality levels, and the ATC speech data with different quality levels is inputted into a to-be-evaluated ATC speech recognition system.

In step S2, an evaluation index system is constructed, and indexes of the evaluation index system include utility layer indexes and support layer indexes.

In step S3, weights of the utility layer indexes and weights of the support layer indexes under the ATC speech data with different quality levels are determined by using an improved group-G2 method and an improved criteria importance through intercriteria correlation (CRITIC) method.

In step S4, scores of a utility layer and scores of a support layer under the ATC speech data with different quality levels are calculated according to the weights of the utility layer indexes and the weights of the support layer indexes under the ATC speech data with different quality levels and by using an improved technique for order preference by similarity to ideal solution (TOPSIS) method, the scores of the utility layer are added to obtain a comprehensive score of the utility layer, and the scores of the support layer are added to obtain a comprehensive score of the support layer. The TOPSIS method is to determine Z⁺ and Z⁻ of an index through a statistical method, to thereby score a recognition performance of the to-be-evaluated ATC speech recognition system. Z⁺ represents a set containing target values of evaluation indexes of the to-be-evaluated ATC speech recognition system, and Z⁻ represents a set containing worst values of the evaluation indexes of the to-be-evaluated ATC speech recognition system.

In step S5, a weight of the utility layer and a weight of the support layer of the to-be-evaluated ATC speech recognition system are determined according to the comprehensive score of the utility layer and the comprehensive score of the support layer and by using the improved group-G2 method and the improved CRITIC method.

In step S6, the weight of the utility layer and the comprehensive score of the utility layer are multiplied to obtain a first product, the weight of the support layer and the comprehensive score of the support layer are multiplied to obtain a second product, and the first product and the second product are added to obtain a comprehensive score of the to-be-evaluated ATC speech recognition system.

In step S7, a level of the recognition performance of the to-be-evaluated ATC speech recognition system is determined according to the comprehensive score of the to-be-evaluated ATC speech recognition system.

The improved group-G2 method is a subjective weighting method with high flexibility and can largely reflect risk awareness characteristics of experts, and the improved group-G2 method includes the following steps (1)-(4).

In step (1), a weight of a minimum class non empty intersection (i.e., a comprehensive assignment interval of an expert for indexes) is calculated as follows.

1) The Minimum Class Non Empty Intersection.

x_i represents an evaluation index, x_m represents a least important index recognized by the experts, a_i^(k) represents a ratio of importance degree between the index x_i and the index x_m by an expert k. D_i^(k) represents an assignment interval of a_i^(k), where d_1i^(k) and d_2i^(k) are respectively left and right end points of the interval, for the index m, d_1m^(k)=d_2m^(k)=1 (the least important index is set as 1). Therefore, a minimum class non empty intersection of D_i^(k) is set as D_i^A, let a set S={1, 2, 3, . . . , L (L experts)}, and the minimum class non empty intersection D_i^A should satisfy the following conditions 1-3.

In condition 1, A represents a non-empty subset of S.

In condition 2, D_i^A=∩_k∈AD_i^(k)≠Ø.

In condition 3, w∈S exists, and w∉A, and all of this satisfy D_i^A∩D_i^(w)=Ø.

2) Adjacency degree: n is set as a number of the minimum class non empty intersections of an i-th index, D_i^Aj is recorded as a j-th minimum class non empty intersection for the i-th index, x_xij⁻ and x_xij⁺ are respectively left and right end points of D_i^Aj, I_j represents an adjacency degree between the j-th minimum class non empty intersection and other minimum class non empty intersections, and calculation formulas of the adjacency degree are expressed as follows:

$I_j=1/{\sum }_{p=1,p\ne j}^nD_i^{A_j}-D_i^{A_p};$ $D_i^{\mathrm{Aj}}-D_i^{\mathrm{Ap}}={\left[{\left(x_{x_{\mathrm{ij}}}^{-}-x_{\mathrm{ip}}^{-}\right)}^2+{\left(x_{x_{\mathrm{ij}}}^{+}-x_{\mathrm{ip}}^{+}\right)}^2\right]}^{1/2}.$

3) Capacity: P_j is set as a capacity of D_i^Aj, that is, A_j represents a number of intervals obtained by how many intervals intersect.

4) A calculation formula of a weight of the minimum class non empty intersection is expressed as follows:

$v_j=P_j{1}_j{\sum }_{i=1}^n{P}_i{1}_i,j=1,2,3,\dots ,n;$

where v_j represents a weight of the j-th minimum class non empty intersection.

In step (2), the comprehensive assignment interval of the expert for the index is calculated as follows.

A formula of the comprehensive assignment interval of the group experts on a comprehensive importance of an index is expressed as follows:

$D_i={\sum }_{j=1}^n{v}_j{D}_i^{{ }_{}\mathrm{Aj}},i=1,2,\dots ,m;$

where n represents a number of the minimum class non-empty intersections, and m represents a number of indexes.

In step (3), a risk attitude factor is determined as follows.

I_k* represents an adjacency degree between the assignment interval D_i^(k) of the ratio of importance degree between the index x_i and the index x_m by the expert and other assignment intervals, and calculation formulas of I_k* are expressed as follows:

$I_k^{{ }_{}*}=1/{\sum }_{j=1.j\ne k}^nD_i^{{ }_{}\left(k\right)}-D_i^{{ }_{}\left(j\right)},k=1,2,\dots ,L;$ $r_k=I_k^{{ }_{}*}/{\sum }_{j=1}^n{I}_j^{{ }_{}*},k=1,2,\dots ,L;$

where r_k represents a result of normalizing the adjacency degree of the assignment interval of the k-th expert.

A calculation formula of a risk attitude factor λ_i is expressed as follows:

${\lambda }_i={\left[-{\sum }_{h=1}^{Q_i^{{ }_{}L}}\frac{{\mathrm{LS}}_i^{{ }_{}h}}{d_1}{r}_{\mathrm{Nh}}+{\sum }_{h=1}^{Q_i^{{ }_{}R}}\frac{{\mathrm{RS}}_i^{{ }_{}h}}{d_2}{r}_{\mathrm{Mh}}\right]}^{\frac{1}{2}};$

• • where r_Nh represents a normalized adjacency degree of an assignment interval of a Nh-th expert, r_Mh represents a normalized adjacency degree of an assignment interval of a Mh-th expert, r_Nh=(1, 2, . . . , Q_i^L) and r_Mh=(1, 2, . . . , Q_i^R), which correspond to r_k (k=1, 2, 3, . . . , L) one-by-one.

Relevant concepts involved in the above formula are as follows.

1) n(D_i^(k)) represents a middle point of the assignment interval D_i^(k); and Q_i^L represents a number of middle points on a left of n(D_i).

2) n(D_i^(Nh)) represents a h-th middle point on an assignment interval to the left of n(D_i) from left to right.

3) Q_i^R represents a number of middle points on a right of n(D_i), and contains n(D_i); and Q_i^L+Q_i^R=L.

4) n(D_i^(Mh)) represents a h-th middle point on an assignment interval to the right of n(D_i) from left to right, and contains a middle point of the interval n(D_i).

5) LS_i^h represents a distance between n(D_i^(Nh)) and n(D_i), RS_i^h represents a distance between n(D_i^(Mh)) and n(D_i), LS_i^h/d₁ represents a left deviation distance between n(D_i^(Nh)) and n(D_i), and h=1, 2, 3, . . . , Q_i^L; RS_i^h/d₂ represents a right deviation distance between n(D_i^(Mh)) and n(D_i); and d₁=n(D_i)−n(D_i^(N1)).

6) n(D_i^(N1)) represents a middle point on the left of n(D_i) and facing away from n(D_i); d₂=n(D_i^(Mh))−n(D_i), and h=Q_i^R; and n(D_i^(Mh)) represents a middle point on the right of n(D_i) and facing away from n(D_i).

In step (4), a weight of the index is determined as follows.

• • f_λi; is set as an interval mapping function of the group experts for the risk attitude factor λ_i; and e (D_i) represents a length of the interval D_i, and a formula of f_λi is expressed as follows:

$f_{{\lambda }_i}\left(D_i\right)=n\left(D_i\right)+{\lambda }_{i}e\left(D_i\right).$

w_i is set as a weight of the i-th index, and a formula of w_i is expressed as follows:

$w_i=f_{{\lambda }_i}\left(D_i\right)/{\sum }_{j=1}^m{f}_{{\lambda }_i}\left(D_i\right).$

The improved CRITIC method is an objective weighting method, an existing entropy weighting method only considers an information entropy value of data, while an existing CRITIC method only considers fluctuation and conflict of data. Therefore, the improved CRITIC method is needed, the improved CRITIC method fully considers the fluctuation, the conflict and the information entropy value of the data, and the improved CRITIC method includes the following steps (1)-(7).

In step (1), an evaluation index matrix is constructed.

In step (2), data is standardized, which mainly makes performance normalized and standardized.

In step (3), a proportion of an i-th object of a j-th index is calculated, and a calculation formula is expressed as follows:

$P_{\mathrm{ij}}=\frac{x_{\mathrm{ij}}}{{\sum }_{i=1}^m{x}_{\mathrm{ij}}}.$

In step (4), an information entropy of the j-th index is calculated, and a calculation formula is expressed as follows:

$e_j=-\frac{1}{\ln n}{\sum }_{i=1}^m{P}_{\mathrm{ij}}\ln P_{\mathrm{ij}}.$

In step (5), a standard deviation of data is calculated, and calculation formulas are expressed as follows:

${\stackrel{\_}{x}}_j=\frac{1}{m}{\sum }_{i=1}^m{x}_{\mathrm{ij}};S_j=\sqrt{\frac{{\sum }_{i=1}^m{\left(x_{\mathrm{ij}}-{\stackrel{\_}{x}}_j\right)}^2}{n-1}}.$

In step (6), a correlation matrix of the data is calculated, and a calculation formula is expressed as follows:

$R=\frac{{\sum }_{j,k=1}^n\left(x_{\mathrm{ij}}-{\stackrel{\_}{x}}_j\right)\left(x_{\mathrm{ik}}-{\stackrel{\_}{x}}_k\right)}{\sqrt{{\sum }_{j=1}^n{\left(x_{\mathrm{ij}}-{\stackrel{\_}{x}}_j\right)}^2{\sum }_{k=1}^n{\left(x_{\mathrm{ik}}-{\stackrel{\_}{x}}_k\right)}^2}}.$

In step (7), a weight is calculated, and a calculation formula is expressed as follows:

$W_j=\frac{\left(e_j+S_j\right){\sum }_{i=1}^n\left(1-❘r_{\mathrm{ij}}❘\right)}{{\sum }_{j=1}^n\left(e_j+S_j\right){\sum }_{i=1}^n\left(1-❘r_{\mathrm{ij}}❘\right)};$

where r_ij represents a correlation coefficient of the i-th index and the j-th index.

A fusion formula of a subjective weight and an objective weight is expressed as follows:

$W_k=\frac{w_{\mathrm{ok}}{w}_{\mathrm{sk}}}{{\sum }_{k=1}^m{w}_{\mathrm{ok}}{w}_{\mathrm{sk}}};$

where w_ok represents an objective weight of an index k, and w_sk represents a subjective weight of the index k.

An introduction of the improved TOPSIS method is as follows.

(1) The TOPSIS method before improving includes the following steps 1) to 2).

In step 1), normalizations of the indexes are as follows.

In the utility layer, accuracy indexes are extremely large indexes, a processing time consumption index is an extremely small index, and it is necessary to convert the processing time consumption index to an extremely large index. In the support layer, a maintainability exponent, a mean time between failures (MTBF) and a mean time to failure (MTTF) are extremely large indexes, and a module coupling degree and a mean time to repair (MTTR) are extremely small indexes, and it is necessary to convert the module coupling degree and the mean time to repair to extremely large indexes.

In step 2), standardization of the data is as follows.

In order to eliminate effects of dimensions of different indexes, it is necessary to normalize and standardize the index data matrix, thus obtaining a usable evaluation index matrix, a score of each evaluation object is calculated according to a scoring formula, and the score is calculated based on the following distance formula:

$\mathrm{score}\mathrm{of}x=\frac{\mathrm{distance}\mathrm{between}x\mathrm{and}\mathrm{minimum}}{\begin{array}{c}\mathrm{distance}\mathrm{between}x\mathrm{and}\mathrm{maximum}+\\ \mathrm{distance}\mathrm{between}x\mathrm{and}\mathrm{minimum}\end{array}}.$

Specifically, a maximum and a minimum of an index are found and recorded as Z_j⁺ and Z_j⁻, and a distance formula of a j-th index of an i-th evaluation object and the maximum is expressed as follows:

$D_i^{{ }_{}+}=\sqrt{{\sum }_{j=1}^m{w_j\left(Z_j^{{ }_{}+}-z_{\mathrm{ij}}\right)}^2};$

where Z⁺ represents a maximum of the j-th index, and z_ij represents a j-th normalized and standardized index value of the i-th evaluation object.

Similarly, a distance formula of the j-th index of the i-th evaluation object and the minimum is expressed as follows:

$D_i^{{ }_{}-}=\sqrt{{\sum }_{j=1}^m{w_j\left(Z_j^{{ }_{}-}-z_{\mathrm{ij}}\right)}^2};$

where w_j represents a weight of the index j; and a score S_i of the i-th evaluation object is calculated as follows:

$S_i=\frac{{D_i}^{-}}{{D_i}^{+}+{D_i}^{-}}.$

(2) The improved TOPSIS method includes the following steps 1) to 3).

In step 1), indexes are normalized.

In step 2), data is standardized.

In step 3), a score is calculated.

Formulas proposed in the TOPSIS method before improving is only applicable when there are multiple objects to be evaluated, calculation results of different batches of evaluation objects may not be comparable due to a lack of unified standards during calculation, and scoring results vary greatly when calculated in different batches for a same system.

In the embodiment, determination formulas of Z⁺ and Z⁻ of the index determined by using the improved TOPSIS method and a statistical method are expressed as follows:

$Z^{+}=\left(\max \left\{z_{11},z_{21},\dots ,z_{n1}\right\},\max \left\{z_{12},z_{22},\dots ,z_{n2}\right\},\dots \max \left\{z_{1n},z_{2n},\dots ,z_{\mathrm{nn}}\right\}\right);$ $Z^{-}=\left(\min \left\{z_{11},z_{21},\dots ,z_{n1}\right\},\min \left\{z_{12},z_{22},\dots ,z_{n2}\right\},\dots \min \left\{z_{1n},z_{2n},\dots ,z_{\mathrm{nn}}\right\}\right).$

For evaluating the speech recognition system, the index value is fixed, Z⁺ and Z⁻ of the index are determined by the statistical method, and a recognition performance of each to-be-evaluated objects is scored by using the statistical method, so as to achieve scoring for a single object, and the to-be-evaluated objects of different batches have comparability due to unified standards.

A level of the recognition performance of the speech recognition system is graded according to the comprehensive score of the speech recognition system obtained by the method of the disclosure, which are divided into levels 1-5, and level 5 is the highest level. In response to the comprehensive score being a range of 0.0-0.3 scores, containing 0.3 scores, a level of the recognition performance is level 1; in response to the comprehensive score being a range of 0.3-0.5 scores, containing 0.5 scores, a level of the recognition performance is level 2; in response to the comprehensive score being a range of 0.5-0.7 scores, containing 0.7 scores, a level of the recognition performance is level 3; in response to the comprehensive score being a range of 0.7-0.9 scores, containing 0.9 scores, a level of the recognition performance is level 4; and in response to the comprehensive score being a range of 0.9-1.0 scores, containing 1.0 score, a level of the recognition performance is level 5.

A specific recognition performance of each level is as follows.

When the level of the recognition performance is level 1, the recognition system has a low vocabulary and a slow recognition speed, a recognition result does not meet a standard specification, the recognition system can only effectively recognize specific input audio, robustness of the recognition system is poor, and the recognition performance cannot meet requirements.

When the level of the recognition performance is level 2, the recognition system has a limited vocabulary and a slow recognition speed, a grammar structure of a recognition result deviates significantly from the standard specification, the recognition system is more susceptible to interference from audio, resulting in significant deviations in the recognition result, and requires a high standard for input speech, resulting in a poor overall recognition performance.

When the level of the recognition performance is level 3, a vocabulary of the recognition system meets a basic requirement, a grammar structure of a recognition result basically meets the standard specification, a recognition speed is normal, some insertion and deletion errors occur occasionally, and some words are ambiguous.

When the level of the recognition performance is level 4, a vocabulary of the recognition system has a wide coverage, a grammar structure of a recognition result meets the standard specification, a recognition speed is fast, and some words are ambiguous.

When the level of the recognition performance is level 5, a vocabulary of the recognition system has a wide coverage, a grammar structure of a recognition result meets the standard specification, a recognition speed is fast, and recognition is correct and is not ambiguous.

In an embodiment, in step 1, the obtained ATC speech data is graded to 5 levels according to a mean opinion score (MOS) terminology, and a gradation state is shown in Table 1.

TABLE 1
An evaluation standard for grading the ATC speech data
Audio level	MOS value	Evaluation standard
Excellent	4.0-5.0	Excellent, listen clearly, low latency, and smooth communication
Good	3.5-4.0	Second best, listen clearly, low latency, lack of smooth
		communication, and a bit of noise
Medium	3.0-3.5	Not bad, unable to listen clearly, a certain delay, and be able to
		communicate
Poor	1.5-3.0	Reluctant, cannot listen clearly, large delay, and communication
		requires multiple repetitions
Bad	0-1.5	Extremely poor, unintelligible, large delay, and bad communication

In an embodiment, in step 2, the utility layer indexes include the accuracy indexes and a recognition rate index, the accuracy indexes include a sentence accuracy, a word accuracy and a keyword accuracy, and the utility layer indexes are shown in FIG. 2.

An expression for calculating the sentence accuracy is expressed as follows:

$S_{\mathrm{acc}}=1-\left(\frac{E_s}{S_t}\times 100\%\right);$

where S_acc represents the sentence accuracy, E_s represents a number of misrecognized sentences, and S_t represents a total number of recognized sentences.

An expression for calculating the word accuracy is expressed as follows:

$W_{\mathrm{acc}}=\frac{T}{N}\times 100\%;$

where W_acc represents the word accuracy, T represents a number of correctly recognized words, and N represents a total number of words.

An expression for calculating the keyword accuracy is expressed as follows:

$K_{\mathrm{acc}}=1-\left(\frac{K_{\mathrm{error}}}{K_{\mathrm{total}}}\times 100\%\right);$

where K_acc represents the keyword accuracy, K_error represents a number of keyword errors in a sentence, and K_total represents a total number of keywords in a sentence

The recognition rate index includes a processing time consumption, and the processing time consumption refers to a time consumed from a moment when the ATC speech recognition system receives completely inputted ATC speech data to a moment when the ATC speech recognition system recognizes a result and outputs the result.

In an embodiment, in step S2, the support layer indexes include reliability indexes and maintainability indexes.

The reliability indexes include a MTTF, a MTTR and a MTBF.

As shown in FIG. 3, the MTTF refers to a mean time for the speech recognition system to operate without failures, which is a mean time of time periods between a start of normal operation and an occurrence of a malfunction of the speech recognition system, and a calculation formula is expressed as follows:

$\mathrm{MTTF}=\frac{\sum T_1}{n};$

where T₁ represents a time to operate without failures, and n represents a number of time periods between the start of normal operation and the occurrence of the malfunction of the speech recognition system.

The MTTR refers to a mean time of time periods between the occurrence of the malfunction and an end of maintenance of the speech recognition system, and a calculation formula is expressed as follows:

$\mathrm{MTTR}=\frac{\sum \left(T_2+T_3\right)}{n};$

• • where T₂ represents a time taken from the start of the malfunction to a start of maintenance, T₃ represents a time of maintenance, and n represents a number of time periods between the occurrence of the malfunction and the end of maintenance.

The MTBF refers to a mean time of time periods between two failures of the speech recognition system, and a calculation formula is expressed as follows:

$\mathrm{MTBF}=\frac{\sum \left(T_2+T_3+T_1\right)}{n};$

where T₁ represents the time to operate without failures, T₂ represents the time taken from the start of the malfunction to a start of maintenance, T₃ represents the time of maintenance, and n represents a number of time periods between two failures.

The maintainability indexes include a maintainability exponent and a module coupling degree.

A calculation formula of the maintainability exponent is expressed as follows:

$171-5.2\ln \left(\mathrm{HV}\right)-0.23\mathrm{CC}-16.2\ln \left(\mathrm{LOC}\right)+50\sin \sqrt{2.46\times \mathrm{COM}};$

where HV represents a Halstead value, CC represents a cyclomatic complexity, LOC represents an average number of code lines for each module, and COM represents a percentage of comment lines for each module.

A calculation formula of the module coupling degree is expressed as follows:

$\mathrm{Coupling}\left(C\right)=1-\frac{1}{d_i+2c_i+d_o+2c_o+g_d+2g_c+r+w};$

where d_i represents a number of inputted data parameters, c_i represents a number of inputted control parameters, d_o represents a number of outputted data parameters, c_o represents a number of global couplings of outputted control parameters, g_d represents a global variable for storing data, g_c represents a global variable for controlling environmental coupling, w represents a number of fan-out modules called by the module, and r represents a number of fan-in modules calling the modules.

In an embodiment, in step S3, the weights of the utility layer indexes and the weights of the support layer indexes under the ATC speech data with different quality levels are determined by using the improved group-G2 method and the improved CRITIC method, and step 3 specifically includes the following steps S31-S33.

In step S1, a subjective weight of each utility layer index is calculated by using the improved group-G2 method, the improved group-G2 method is a subjective weighting method for inflecting risk awareness characteristics of the experts, and formulas are expressed as follows:

$w_i=f_{{\lambda }_i}\left(D_i\right)/{\sum }_{j=1}^m{f}_{{\lambda }_i}\left(D_j\right);$ $f_{{\lambda }_i}\left(D_i\right)=n\left(D_i\right)+{\lambda }_{i}e\left(D_i\right);$

where f_λi represents the interval mapping function of the group experts for the risk attitude factor λ_i, e(D_i) represents a length of the interval D_i, and w_i represents the subjective weight of the i-th index.

In step S2, an objective weight of each utility layer index under the ATC speech data with different quality levels is calculated by using the improved CRITIC method, the improved CRITIC method is an objective weighting method for the fluctuation, the conflict and the information entropy value of the data, and formulas are expressed as follows:

$W_j=\frac{\left(e_j+S_j\right){\sum }_{i=1}^n\left(1-❘r_{\mathrm{ij}}❘\right)}{{\sum }_{j=1}^n\left(e_j+S_j\right){\sum }_{i=1}^n\left(1-❘r_{\mathrm{ij}}❘\right)};$ $e_j=-\frac{1}{\ln n}{\sum }_{i=1}^m{P}_{\mathrm{ij}}\ln P_{\mathrm{ij}};$ $S_j=\sqrt{\frac{{\sum }_{i=1}^m{\left(x_{\mathrm{ij}}-{\stackrel{\_}{x}}_j\right)}^2}{n-1}};$ ${\stackrel{\_}{x}}_j=\frac{1}{m}{\sum }_{i=1}^m{x}_{\mathrm{ij}};$ $P_{\mathrm{ij}}=\frac{x_{\mathrm{ij}}}{{\sum }_{i=1}^m{x}_{\mathrm{ij}}};$

where P_ij represents the proportion of the i-th object of the j-th index, e_j represents the information entropy value of the j-th index, S_j represents a standard deviation of the data, r_ij represents a correlation coefficient of the i-th index and the j-th index, and W_j represents an objective weight.

In step S33, the weight obtained by the subjective weighting method (i.e., the improved group-G2 method) and the weight obtained by the objective weighting method (i.e., the improved CRITIC method) are fused to obtain a comprehensive weights of each utility layer index and a comprehensive weight of each support layer index under the ATC speech data with different quality levels, and a formula is expressed as follows:

$W_k=\frac{w_{\mathrm{ok}}{w}_{\mathrm{sk}}}{{\sum }_{k=1}^m{w}_{\mathrm{ok}}{w}_{\mathrm{sk}}};$

where w_ok represents the objective weight of the index k, and w_sk represents the subjective weight of the index k.

The evaluation method of the ATC speech recognition system is adopted in the embodiment, four speech recognition systems are actually simulated for instance validation by using a Matlab 2020 software, a system 1, a system 2 and a system 3 are used to calculate weights, and a system 4 is used to final score. Performance of the utility layer indexes and the support layer indexes of each system under the ATC speech data with five quality levels are listed as follows.

The performance of the utility layer indexes of the systems under the ATC speech data with different quality levels is as follows.

TABLE 2
Performance of the utility layer indexes of
the systems under level 1 ATC speech data
	Sentence	Word	Keyword	Processing time
	accuracy	accuracy	accuracy	consumption (s)
System 1	85%	87%	89%	1.8
System 2	88%	88.5%	89.5%	2
System 3	89%	90%	90%	2.1
System 4	91%	91.5%	92%	1.8

TABLE 3
Performance of the utility layer indexes of
the systems under level 2 ATC speech data
	Sentence	Word	Keyword	Processing time
	accuracy	accuracy	accuracy	consumption (s)
System 1	87%	88%	90%	1.6
System 2	89%	89.5%	91%	1.8
System 3	90%	91%	91%	1.9
System 4	92%	93%	93%	1.6

TABLE 4
Performance of the utility layer indexes of
the systems under level 3 ATC speech data
	Sentence	Word	Keyword	Processing time
	accuracy	accuracy	accuracy	consumption (s)
System 1	89%	89.5%	90%	1.5
System 2	90.5%	91%	91%	1.74
System 3	91%	90%	91.5%	1.87
System 4	94%	94%	95%	1.3

TABLE 5
Performance of the utility layer indexes of
the systems under level 4 ATC speech data
	Sentence	Word	Keyword	Processing time
	accuracy	accuracy	accuracy	consumption (s)
System 1	92%	92%	93%	1.35
System 2	93%	93%	94%	1.45
System 3	94%	94.5%	95%	1.55
System 4	95%	95.5%	96%	1.2

TABLE 6
Performance of the utility layer indexes of
the systems under level 5 ATC speech data
	Sentence	Word	Keyword	Processing time
	accuracy	accuracy	accuracy	consumption (s)
System 1	93%	94%	94%	1.3
System 2	94%	94.5%	95%	1.5
System 3	95%	96%	96%	1.6
System 4	97%	97%	97%	1.2

Performance of the support layer indexes of the systems is as follows.

TABLE 7
Performance of the support layer indexes of the systems
		Module
	Maintainability	coupling	MTTF	MTTR	MTBF
	exponent	degree	(h)	(h)	(h)
System 1	89	0.78	8000	30	8030
System 2	80	0.82	8300	36	8330
System 3	75	0.95	7200	401	7240
System 4	90	0.3	13000	20	13020

The improved group-G2 method and the improved CRITIC method are combined to calculate the subjective weight and the objective weight of each utility layer index under the ATC speech data with different quality levels, and the subjective weight and the objective weight are fused to obtain the comprehensive weight of each utility layer index under the ATC speech data with different quality levels; and a same method is used to determine the comprehensive weight of each support layer index.

The improved group-G2 method is used to calculate the weight of each utility layer index.

TABLE 8
Weights of the utility layer indexes under 5 levels ATC speech
data calculated by using the improved group-G2 method
				Minimum
	Assignment	Assignment	Assignment	class non		Risk	Interval
	interval	interval	interval	empty	Comprehensive	attitude	mapping	Index
Index	Di(1) of an	Di(2) of an	Di(3) of an	intersection	assignment	factor	function	weight
xi	expert 1	expert 2	expert 3	DiAJ	interval	λi	ƒλi	wi
Index 1	[1, 1]	[1, 1]	[1, 1]	[1, 1]	[1, 1]	0	1	0.106
Index 2	[1.5, 2.5]	[2, 3]	[1.5, 3]	[2, 2.5]	[2, 2.5]	0	2.25	0.238
Index 3	[1.5, 2.5]	[1.5, 2.5]	[1.5, 2.5]	[2.5, 3]	[2.5, 3]	−0.6	2.45	0.259
Index 4	[1.5, 2.5]	[1.5, 2.5]	[1.5, 2.5]	[3.5, 4]	[3.5, 4]	0	3.75	0.397

Specifically, the index 1 is the sentence accuracy; the index 2 is the word accuracy; the index 3 is the keyword accuracy; and the index 4 is the processing time consumption.

Similarly, the weights of the utility layer indexes under 4 levels ATC speech data obtained by using the improved group-G2 method are shown in Table 9.

TABLE 9
The weights of the utility layer indexes under the 4 levels ATC
speech data obtained by using the improved group-G2 method
Level of the ATC	Weight of the	Weight of the	Weight of the	Processing time
speech data	sentence accuracy	word accuracy	keyword accuracy	consumption (s)
Level 1 ATC	0.17	0.295	0.34	0.195
speech data
Level 2 ATC	0.16	0.29	0.32	0.23
speech data
Level 3 ATC	0.14	0.26	0.3	0.3
speech data
Level 4 ATC	0.104	0.242	0.286	0.368
speech data

TABLE 10
Weights of the support layer indexes calculated by using the improved group-G2 method
				Minimum
	Assignment	Assignment	Assignment	class non		Risk	Interval
	interval	interval	interval	empty	Comprehensive	attitude	mapping	Index
Index	Di(1) of the	Di(2) of the	Di(3) of the	intersection	assignment	factor	function	weight
xi	expert 1	expert 2	expert 3	DiAJ	interval	λi	ƒλi	wi
Index 1	[1, 1]	[1, 1]	[1, 1]	[1, 1]	[1, 1]	0	1	0.062
Index 2	[2, 3]	[2.5, 3]	[2.6, 3.6]	[2.6, 3]	[2.6, 3]	−0.0875	2.66	0.164
Index 3	[4, 6]	[3, 6]	[5, 6.5]	[5, 6]	[5, 6]	−0.222	5.278	0.326
Index 4	[3, 4]	[3, 4.5]	[2, 3.5]	[3, 3.5]	[3, 3.5]	0.184	3.342	0.207
Index 5	[3, 5]	[4, 5.5]	[3, 5]	[4, 5]	[4, 5]	−0.6	3.9	0.2411

Weights of the utility layer indexes and the support layer indexes calculated by using the improved CRITIC method are as follows.

The weights of the utility layer indexes under the ATC speech data with different quality levels calculated by using the improved CRITIC method are shown in Table 11-15.

TABLE 11
Weights of the utility layer indexes under the level 1 ATC
speech data calculated by using the improved CRITIC method
				Processing time
	Sentence accuracy	Word accuracy	Keyword accuracy	consumption (s)
Information	0.6290196609534563	0.5881199233394943	0.5881199233394943	0.5220605575349556
entropy
Information	0.88142193	0.82515617	0.82515617	2.47773866
carrying
capacity
Objective	0.26374510	0.25014913	0.25014913	0.23595663
weight

The information carrying capacity is obtained by multiplying conflict and volatility of data.

TABLE 12
Weights of the utility layer indexes under the level 2 ATC
speech data calculated by using the improved CRITIC method
				Processing time
	Sentence accuracy	Word accuracy	Keyword accuracy	consumption (s)
Information	0.6203819994751361	0.5881199233394943	0.6374906669220128	0.5220605575349553
entropy
Information	0.86187345	0.87119148	1.00596527	2.46404423
carrying
capacity
Objective	0.25637963	0.24340510	0.26816446	0.23205081
weight

TABLE 13
Weights of the utility layer indexes under the level 3 ATC
speech data calculated by using the improved CRITIC method
				Processing time
	Sentence accuracy	Word accuracy	Keyword accuracy	consumption (s)
Information	0.6290196609534563	0.5220605575349555	0.6203819994751361	0.5316247532311797
entropy
Information	1.02890285	1.00022968	1.04098174	2.26830129
carrying
capacity
Objective	0.27816315	0.20498861	0.27058092	0.24626732
weight

TABLE 14
Weights of the utility layer indexes under the level 4 ATC
speech data calculated by using the improved CRITIC method
				Processing time
	Sentence accuracy	Word accuracy	Keyword accuracy	consumption (s)
Information	0.5881199233394949	0.554099538248139	0.5881199233394933	0.5881199233394943
entropy
Information	0.81919132	0.82463451	0.81919132	2.44679501
carrying
capacity
Objective	0.25208077	0.24375768	0.25208077	0.25208077
weight

TABLE 15
Weights of the utility layer indexes under the level 5 ATC
speech data calculated by using the improved CRITIC method
				Processing time
	Sentence accuracy	Word accuracy	Keyword accuracy	consumption (s)
Information	0.5881199233394933	0.46676943658628967	0.5881199233394943	0.5220605575349556
entropy
Information	0.82515617	0.83687839	0.82515617	2.43415730
carrying
capacity
Objective	0.26240273	0.23095275	0.26240273	0.24424179
weight

Weights of the support layer indexes calculated by using the improved CRITIC method is shown in Table 16.

TABLE 16
Weights of the support layer indexes calculated by using the improved CRITIC method
	Maintainability	Module coupling
	exponent	degree	MTTF (h)	MTTR (h)	MTBF (h)
Information	0.5345474455983533	0.6301647186683301	0.6270410695481976	0.5540995382481381	0.6264291625127868
entropy
Information	0.34189017	0.15893173	0.33769637	0.30453009	0.34240899
carrying
capacity
Objective	0.18331129	0.21888241	0.20460045	0.18926360	0.20394226
weight

Comprehensive weights of the utility layer indexes are calculated as follows.

TABLE 17
Dynamic comprehensive weights of the utility layer indexes under levels 1-5 speech data
	Weight of the sentence	Weight of the word	Weight of the	Processing time
	accuracy	accuracy	keyword accuracy	consumption (s)
Level 1	0.17956724308621672	0.2955390047654	0.340621225831423	0.18427252631686084
speech data
Level 2	0.16356444213165897	0.28145765773482057	0.3421657976449812	0.21281210248853927
speech data
Level 3	0.15747565905989744	0.2155206467724271	0.3282496161954392	0.2987540779722362
speech data
Level 4	0.10483767901836524	0.23589460869306872	0.2883036173005044	0.3709640949880616
speech data
Level 5	0.11228833302739928	0.22190164162571346	0.2743648891895888	0.3914451361572985
speech data

TABLE 18
Fixed comprehensive weights of the support layer indexes
Maintainability	Module coupling
exponent	degree	MTTF (h)	MTTR (h)	MTBF (h)
0.15132828144297342	0.06831083432737761	0.33574629044719095	0.1972079780724709	0.24740661570998718

An optimal value and a worst value of each index obtained from simulation statistics are substituted into the improved TOPSIS method to calculate scores of the utility layers and the support layers of the system 1, the system 2 and the system 3 under 5 level speech data, and a fixed comprehensive weight of the utility layer and a fixed comprehensive weight of the support layer are calculated by using the improved group-G2 method and the improved CRITIC method.

Optimal values and worst values of the utility layer indexes under level 1 speech data are shown in Table 19.

TABLE 19
Optimal values and worst values of the utility
layer indexes under level 1 speech data
	Sentence	Word	Keyword	Processing time
	accuracy	accuracy	accuracy	consumption (s)
Optimal value	96%	96%	96%	0.9
Worst value	80%	80%	80%	5

Optimal values and worst values of the utility layer indexes under level 2 speech data are shown in Table 20.

TABLE 20
Optimal values and worst values of the utility
layer indexes under level 2 speech data
	Sentence	Word	Keyword	Processing time
	accuracy	accuracy	accuracy	consumption (s)
Optimal value	97%	97%	97%	0.8
Worst value	82%	82%	82%	4.5

Optimal values and worst values of the utility layer indexes under level 3 speech data are shown in Table 21.

TABLE 21
Optimal values and worst values of the utility
layer indexes under level 3 speech data
	Sentence	Word	Keyword	Processing time
	accuracy	accuracy	accuracy	consumption (s)
Optimal value	98%	98%	98%	0.7
Worst value	83%	83%	83%	4

Optimal values and worst values of the utility layer indexes under level 4 speech data are shown in Table 22.

TABLE 22
Optimal values and worst values of the utility
layer indexes under level 4 speech data
	Sentence	Word	Keyword	Processing time
	accuracy	accuracy	accuracy	consumption (s)
Optimal value	99%	99%	99%	0.6
Worst value	85%	85%	85%	3.5

Optimal values and worst values of the utility layer indexes under level 5 speech data are shown in Table 23.

TABLE 23
Optimal values and worst values of the utility
layer indexes under level 5 speech data
	Sentence	Word	Keyword	Processing time
	accuracy	accuracy	accuracy	consumption (s)
Optimal value	99.5%	99.5%	99.5%	0.5
Worst value	87%	87%	87%	3

TABLE 24
Optimal values and worst values of the support layer indexes
		Module
	Maintainability	coupling	MTTF	MTTR	MTBF
	exponent	degree	(h)	(h)	(h)
Optimal value	99	0.1	15000	10	15010
Worst value	60	1	5000	72	5072

Scores of the utility layers of the systems are as follows.

TABLE 25
Scores of the utility layers
	System 1	System 2	System 3
	Level 1 speech data	0.747	0.716	0.7
	Level 2 speech data	0.757	0.717	0.695
	Level 3 speech data	0.744	0.679	0.642
	Level 4 speech data	0.736	0.705	0.69
	Level 5 speech data	0.678	0.6001	0.5616
	Total score	3.662	3.4171	3.2896

Scores of the support layers of the systems are as follows.

TABLE 26
Scores of the support layers of the systems
	System 1	System 2	System 3
	Support layer	0.43	0.396	0.312

Weights of the utility layer indexes and the support layer indexes of the system 4 obtained by using the improved group-G2 method are as follows.

TABLE 27
Weights of the utility layer indexes and the support layer
indexes obtained by using the improved group-G2 method
				Minimum
	Assignment	Assignment	Assignment	class non		Risk	Interval
	interval	interval	interval	empty	Comprehensive	attitude	mapping	Index
Index	Di(1) of the	Di(2) of the	Di(3) of the	intersection	assignment	factor	function	weight
xi	expert 1	expert 2	expert 3	DiAJ	interval	λi	ƒλi	wi
Index 1	[1, 1]	[1, 1]	[1, 1]	[1, 1]	[1, 1]	0	1	0.37
Index 2	[1.5, 2]	[1.4, 1.8]	[1.3, 2.3]	[1.5, 1.8]	[1.5, 1.8]	0.152	1.7	0.63

Weights of the utility layer indexes and the support layer indexes of the system 4 obtained by using the improved CRITIC method are as follows.

	Utility layer	Support layer
Information entropy	0.5264316348395962	0.6255467860676905
Information carrying	0.06278809	0.06358222
capacity
Objective weight	0.47918135	0.52081865

Comprehensive weights of the utility layer and the support layer of the system 4 are calculated as follows.

TABLE 29
Comprehensive weights of the utility layer and the support layer
Utility layer      Support layer
0.6103762499483129 0.38962375005168703

Optimal values and worst values of the scores of the utility layer indexes and the support layer indexes obtained from simulation statistics are substituted into the improved TOPSIS method to calculate a final score of the system 4.

Optimal values and worst values of the scores of the utility layer and the support layer are as follows.

TABLE 30
Optimal values and worst values of the scores
of the utility layer and the support layer
	Score of the utility layer	Score of the support layer
Optimal value	5	1
Worst value	0	0

Scores of the utility layer of the system 4 under levels 1-5 speech data are calculated according to the dynamic weights of the utility layer indexes under the speech data with different quality levels.

TABLE 31
Scores of the utility layer of the system 4 under levels 1-5 speech data
	Level 1	Level 2	Level 3	Level 4	Level 5	Total
	speech data	speech data	speech data	speech data	speech data	score
Score	0.7762	0.7801	0.8161	0.7924	0.721	3.8858

According to the fixed weight of each support layer index, the score of the support layer of the system 4 is calculated as 0.8093. According to the fixed weights of the utility layer and the support layer, the final score of the system 4 is calculated as 0.79. According to the gradation principle of the recognition performance of the speech recognition system, the level of the recognition performance of the to-be-evaluated speech recognition system is determined as level 4.

In an embodiment, an evaluation device of the ATC speech recognition system adopts a Core i7-12700 processor, and a memory adopts a 980 PRO IT solid state disk of Samsung, thus making at least one processor can execute any one steps of the evaluation method.

The above is merely the embodiment of the disclosure, and it is not used to limit the disclosure. Any modification, equivalent substitution and improvement made within a spirit and principle of the disclosure should be included in a scope of protection of the disclosure.

Claims

1. An evaluation method of an air traffic control (ATC) speech recognition system, comprising: S1, obtaining ATC speech data, grading, based on a quality level of the ATC speech data, the ATC speech data to obtain ATC speech data with different quality level, and inputting the ATC speech data with different quality level into a to-be-evaluated ATC speech recognition system;S2, constructing an evaluation index system; wherein indexes of the evaluation index system comprise utility layer indexes and support layer indexes;S3, determining, by using an improved group-G2 method and an improved criteria importance through intercriteria correlation (CRITIC) method, weights of the utility layer indexes and weights of the support layer indexes under the ATC speech data with different quality levels;S4, calculating, according to the weights of the utility layer indexes and the weights of the support layer indexes under the ATC speech data with different quality levels, scores of a utility layer and scores of a support layer under the ATC speech data with different quality levels by using an improved technique for order preference by similarity to ideal solution (TOPSIS) method; and adding the scores of the utility layer to obtain a comprehensive score of the utility layer, and adding the scores of the support layer to obtain a comprehensive score of the support layer; wherein the TOPSIS method is to determine Z+ and Z− of an index through a statistical method, to thereby score a recognition performance of the to-be-evaluated ATC speech recognition system; andwherein Z+ represents a set containing optimal values of evaluation indexes of the to-be-evaluated ATC speech recognition system, and Z− represents a set containing worst values of the evaluation indexes of the to-be-evaluated ATC speech recognition system;S5, determining, according to the comprehensive score of the utility layer and the comprehensive score of the support layer, a weight of the utility layer and a weight of the support layer of the to-be-evaluated ATC speech recognition system by using the improved group-G2 method and the improved CRITIC method;S6, multiplying the weight of the utility layer and the comprehensive score of the utility layer to obtain a first product, multiplying the weight of the support layer and the comprehensive score of the support layer to obtain a second product, and adding the first product and the second product to obtain a comprehensive score of the to-be-evaluated ATC speech recognition system; andS7, determining, according to the comprehensive score of the to-be-evaluated ATC speech recognition system, a level of the recognition performance of the to-be-evaluated ATC speech recognition system.

2. The evaluation method of the ATC speech recognition system as claimed in claim 1, wherein in step S2, the utility layer indexes comprise accuracy indexes and a recognition rate index, the recognition rate index comprises a processing time consumption, and the processing time consumption refers to a time consumed from a moment when the to-be-evaluated ATC speech recognition system receives completely inputted ATC speech data to a moment when the to-be-evaluated ATC speech recognition system recognizes a result and outputs the result.

3. The evaluation method of the ATC speech recognition system as claimed in claim 2, wherein the accuracy indexes comprise a sentence accuracy, a word accuracy and a keyword accuracy, and a calculation expression of the keyword accuracy is expressed as follows:

4. The evaluation method of the ATC speech recognition system as claimed in claim 1, wherein in step S2, the support layer indexes comprise reliability indexes and maintainability indexes.

5. The evaluation method of the ATC speech recognition system as claimed in claim 1, wherein in step S4, calculation formulas of Z+ and Z− of the index in the improved TOPSIS method are as follows:

6. The evaluation method of the ATC speech recognition system as claimed in claim 1, wherein in step S7, the determining, according to the comprehensive score of the to-be-evaluated ATC speech recognition system, a level of the recognition performance of the to-be-evaluated ATC speech recognition system comprises: determining, in response to the comprehensive score being in a range of 0.0-0.3 scores, containing 0.3 scores, the level of the recognition performance to be level 1;determining, in response to the comprehensive score being in a range of 0.3-0.5 scores, containing 0.5 scores, the level of the recognition performance to be level 2;determining, in response to the comprehensive score being in a range of 0.5-0.7 scores, containing 0.7 scores, the level of the recognition performance to be level 3;determining, in response to the comprehensive score being in a range of 0.7-0.9 scores, containing 0.9 scores, the level of the recognition performance to be level 4; anddetermining, in response to the comprehensive score being in a range of 0.9-1.0 scores, containing 1.0 score, the level of the recognition performance to be level 5.

7. An evaluation system of the ATC speech recognition system, comprising: at least one processor, and a memory communicatively connected to the at least one processor; wherein the memory stores an instruction executed by the at least one processor, and the instruction is configured to be executed by the at least one processor, to make the at least one processor to execute the evaluation method as claimed in claim 1.