## 3 Experimental Results  
### Experiment Settings  
#### 3.1.1 Database  
This model aimed to evaluate the quality of air traffic control speech, so it was necessary to construct a real air traffic control speech database. We collected real air traffic control speech from the air traffic control agency's voice communication system (VCS). The speech was encoded using Pulse Code Modulation (PCM). These speech samples covered a wide range of acoustic conditions, including different genders (male/female), various sources of noise (ground noise, aircraft noise, weather noise, etc. ), different speaking rates (slow/medium/fast), and different scenarios (ground, tower, approach, etc.). To create a \begin{table}
\begin{tabular}{c c} \hline \hline
**Network Layer** & **Unit** \\ \hline BiLSTM & 16 \\ Dropout 50\% & - \\ BiLSTM & 32 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Details of the BiLSTM network. Figure 6: Structure of BiLSTM network. Figure 7: Structure of score mapping. database for speech quality assessment in the air traffic control domain, which includes the air traffic control speech and their corresponding real MOS values, we employed various strategies. These included voice activity detection (VAD) to extract valid audio segments, automatic speaker recognition, and manual verification. Finally, we annotated the air traffic control speech with the real MOS values. The specific process is outlined as follows. The method of collecting the air traffic control speech is as follows: based on the existing VCS, an additional bypass VCS is installed at each air traffic control position, and communication speech between pilots and controllers is simultaneously collected through both the bypass VCS and the existing VCS. In this process, the existing VCS is set to normal communication mode, receiving and recording the distorted speech data containing the original noise. The bypass VCS is set to monitoring mode, designed to minimize background noise and interference as much as possible while receiving and recording clean speech data that is theoretically free from disturbances such as noise. Thus, the aforementioned collection of original noisy and clear speech data pairs is achieved [34]. The collected clean-distorted speech data pairs undergo preprocessing, including segmentation and speaker discrimination. The process is as follows:  
1. Continuous communication speech is segmented into command speech segments using VAD, with each segment containing a single-sentence command from one speaker, while non-active speech is discarded. 2. A classification model is employed to classify command speech segments into two categories: controller and pilot speech, with pilot speech data being discarded. The preprocessing procedure is illustrated in Figure 8.  
Figure 8: Preprocessing procedure. Following the mentioned steps, we collected 50,000 pairs of air traffic control speech data from real-world environments, each pair containing both the clean speech data and the distorted speech data from the same air traffic control speech. Finally, the distorted speech data were annotated with the MOS to be used for training the subsequent air traffic control speech quality assessment model. Due to the time-consuming and labor-intensive nature of obtaining the MOS through subjective evaluations, we employed the PESQ model proposed by ITU to derive the MOS of the distorted speech data. PESQ calculates the quality score of the distorted speech data by analyzing the feature differences between the clean speech data and the distorted speech data from the same air traffic control speech. As a result, we obtained an air traffic control speech database comprising 50,000 instances, comprising air traffic control speech with various disturbances including noise, along with the corresponding MOS ratings.