## 1 Introduction
### Related Work  
Various non-intrusive speech quality assessment methods are available in the literature. A non-intrusive speech quality assessment method under complex environments is proposed in [10,11]. The method [10] employs Bayesian non-negative matrix factorization (BNMF) to calculate the fundamental spectro-temporal matrixes of the target speech, integrating the resulting matrix, as a separate layer, into the deep neural network (DNN) model. Subsequently, a deep neural network is trained to learn the complex mapping between the target source and the mixture signal, reconstructing the magnitude spectrogram of the quasi-clean speech. Finally, the reconstructed speech is regarded as the reference of the modified PESQ to estimate the MOS of the tested speech sample. The method [11] learns an overcomplete dictionary of the clean speech power spectrum through K-singular value decomposition. Then, during the sparse representation stage, it adaptively obtains the stopping residue based on the estimated cross-correlation and noise spectrum, adjusted by an a posteriori SNR-weighted factor. It utilizes orthogonal matching pursuit to reconstruct clean speech spectra from noisy speech. By using the quasi-clean speech as a reference, it estimates the degraded speech's MOS using the modified PESQ. Both of these methods entail constructing reference signals, leading to high computational costs. Another approach to non-intrusive speech quality assessment involves extracting intrinsic features from the speech signal and then mapping these features to MOS. Compared to reconstructing clean speech, this method has lower computational costs. [PERSON] et al. [12] proposed an end-to-end non-intrusive speech quality evaluation model based on Bidirectional Long Short-Term Memory (BiLSTM) for predicting speech PESQ scores. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Rating** & **Speech Quality** & **Level of Distortion** \\ \hline
5 & Excellent & Imperceptible \\
4 & Good & Just perceptible, but not annoying \\
3 & Fair & Perceptible and slightly annoying \\
2 & Poor & Annoying, but not objectionable \\
1 & Bad & Very annoying and objectionable \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Speech quality rating scale. However, this model faces difficulties in evaluating enhanced speech with low PESQ scores compared to noisy speech. [PERSON] et al. [13] studied several neural network architectures for evaluating speech conversion quality, including Convolutional Neural Network (CNN), BiLSTM, and CNN-BiLSTM. However, this model is only applied to speech conversion systems and has limited generalization ability. [PERSON] et al. [14] used a pyramid BiLSTM with an attention mechanism to predict MOS, and the proposed model achieved scores close to human judgment. However, the encoder of this model, composed of a stacked pyramid BiLSTM, significantly increases the overall computational complexity, making it less suitable for low-resource speech evaluation tasks. [PERSON] et al. [15] combined modulation energy features with a recurrent neural network using Long Short-Term Memory to propose a non-intrusive speech quality evaluation method suitable for evaluating speech enhancement algorithms under various acoustic conditions. [PERSON] et al. [16] proposed a reference-free speech quality evaluation method based on ResNet and BiLSTM. They utilized an attention mechanism [17] to obtain weights and scored based on the BiLSTM output, resulting in scores closer to human ratings. However, this model mostly predicts MOS scores between 1.5 and 4.5, and it cannot effectively predict speech quality with low or high MOS scores. Most of the aforementioned methods available in the literature show promising results, but, to the best of our knowledge, there is no method available for evaluating the quality of air traffic control speech.