## 1 Introduction  
Currently, air traffic management in the civil aviation sector in China primarily relies on manual control, with radio communication being used for the instructions exchanged between air traffic controllers and pilots. During the process of voice interaction, factors such as noise, accents, speaking rate, intonation, and timbre can affect the transmission of speech information and auditory perception. However, clear and accurate voice communication between controllers and pilots is a crucial factor in ensuring flight safety. Therefore, the evaluation of the quality of air traffic control speech is of utmost importance.  
Speech quality assessment can be broadly categorized into two categories: subjective methods and objective methods. Subjective evaluation, which takes into account the perceptual characteristics of the human auditory system, is widely adopted due to its high credibility and reliability. This method requires professionals to reference the pristine clean version of the speech signal to estimate the quality of the distorted speech signal, typically using a rating scale ranging from 1 to 5 [1], as shown in Table 1. The average of all ratings is referred to as the mean opinion score (MOS) [2]. During the evaluation process, professionals consider various aspects, including noise, distortion, speech clarity, the naturalness of sound, and alignment with specific application scenarios, which allows for a relatively objective assessment of speech quality.  
However, subjective evaluation methods exhibit limitations, encompassing rater selectivity and variability, as well as the time-consuming, costly, and non-repeatable nature of the evaluation process [3]. To address these challenges, researchers have begun proposing objective evaluation methods. Objective evaluation methods aim to quantify and measure speech quality by using computer algorithms or automation techniques, thus reducing the influence of subjective factors [4]. These methods can be based on acoustic features,waveform analysis, signal processing, and other techniques, using mathematical models and algorithms to assess speech quality. Compared to subjective evaluation methods, objective evaluation methods offer better repeatability and consistency, as well as higher efficiency and lower costs.  
Objective evaluation methods can be classified into intrusive and non-intrusive types. Intrusive models also require the pristine clean version of the speech signal as a reference to estimate the quality of distorted speech signals, such as the PESQ model proposed in ITU-T Recommendation P.862 [5,6]. In contrast, non-intrusive models solely rely on the output signal of the transmission system to estimate the MOS, such as the P.563 model introduced by ITU-T [7,8]. Obtaining the pristine clean version of the speech signal can be challenging in practical communication scenarios. Therefore, non-intrusive objective evaluation has become a hot topic in domestic and international research to ensure real-time and convenience. Non-intrusive models can directly assess distorted speech signals, offering greater flexibility to adapt to different application scenarios and reducing the complexity of the evaluation process [9]. This method holds significant importance for a wide range of speech quality assessment tasks.  
With the continuous development of technologies like deep learning, researchers are exploring and improving non-intrusive objective evaluation methods to enhance their accuracy and reliability. These methods leverage models such as neural networks to automatically learn speech features and patterns, enabling more precise speech quality assessment. Future research will continue to focus on the advancement of non-intrusive objective evaluation, further driving its application in practical scenarios.