## 2 ATC Speech Extract by MFCC  
Speech-based fatigue-state detection is a new hotspot in the research on fatigue state management. The implementation process of this method is similar to that of speech emotion recognition. The research on speech-based fatigue-detection technology first needs to establish a speech fatigue database. When the human body is tired, attention and reaction ability decline. The direct manifestation of controller fatigue is that when issuing control instructions, the controller often gets stuck, hesitates, or even issues the wrong instructions, or cannot find the error in the pilot's repeated instructions in time. Therefore, when the controller's voice is stuck, fuzzy and wrong, the controller's state corresponding to the voice is labeled as \"fatigue\". Figure 1 shows the waveform of the same command sent by controllers in different statuses. In recent years, the most widely used feature parameters in speech recognition are spectral correlation features. The commonly used spectral correlation feature is the Mel cepstrum coefficient (MFCC). MFCC was proposed by [PERSON], [PERSON] and [PERSON] in 1937. It is a perceptual scale judged by the audience at an equal distance from each other. Mel is a measure of perceived tone or tone frequency. Above about 500 Hz, the listener judges larger and larger intervals to produce equal pitch increments. The four octaves on the hertz scale above 500 Hz are judged to include about two octaves on the scale. The Mel scale is approximately linear below 1 kHz and logarithmic above. The following formula is used to calculate the hertz (in Hz) of a given frequency \(f\). MFCC has a strong anti-noise  
Figure 1: The signal of voice at different statuses. ability, and the relationship between its Mel frequency (\(f_{mel}\)) and the actual frequency \(f\) of voice is:  
\[f_{mel}(f)=2595\text{lg}(1+f\,/700). \tag{1}\]  
The Figure 2 shows the MFCC feature extraction process. The specific process is as follows:  
(1) Sample continuous signal (voice) at sampling frequency: In the first stage of MFCC, feature extraction is performed to increase the energy at high frequency. This pre-emphasis is accomplished by a digital filter, and the equation is:  
\[H(Z)=1-\alpha Z^{-1}. \tag{2}\]  
where \(\alpha\) represents the pre-weighting factor. (2) Framing: The speech signal is divided into frames and the short-time characteristics of the speech signal are extracted. It is a process of dividing speech samples obtained from analog-to-digital conversion (ADC) into small frames with a time length in the range of 20-40 ms. Framing enables the non-static speech signal to be segmented into quasi-static frames, and the speech signal can be Fourier transformed because the speech signals exhibit quasi-static behavior in a short time of 20-40 ms. The smoothening of the voice signal is performed with the Hamming window:  
\[W(n)=0.54-0.46\ \cos(\frac{2\pi n}{N-1})\ 0\leq n\leq N-1. \tag{3}\]  
(3) Add windows: The windowing step is used to windowize each individual frame, to minimize signal discontinuities at the beginning and end of each frame. (4) FFT: The Fast Fourier transform (FFT) algorithm is ideally used to evaluate the spectrum of speech. FFT converts each frame of n samples from time domain to frequency domain. The linear spectrum of speech signal is obtained by discrete Fourier transform:  
\[X(k)=\sum_{n=0}^{N-1}x(n)e^{-j2 mk/N}\ 0\leq n,k\leq N-1. \tag{4}\]  
(5) Extract the spectrum, Mel filter bank and frequency envelope through Mel frequency filter: Mel filter bank is composed of overlapping triangular filters, and the cut-off frequency is determined by the center frequency of two adjacent filters. The filter has a linearly spaced center frequency and a fixed bandwidth on the Mel scale. Mel frequency filter is designed as follows:  
\[H_{p}(k)= \begin{cases}0&k<f(p-1)\\ \frac{k-f(p-1)}{f(p-)-f(p-1)}&f(p-1)\leq k\leq f(p)\\ \frac{f(p+1)-k}{f(p+1)-f(p)}&f(p)\leq k\leq f(p+1)\\ 0&k>f(p+1)\end{cases} ; \tag{5}\]  
Figure 2: MFCC feature extraction process.