## 3 Vehicle-Embedded Speech Recognition Front-End for Multi-UAV Control
### Noise Reduction for UAV Environments  
#### Conventional Noise Reduction Approaches  
The noise reduction process is performed to eliminate noise components in speech regions. Various noise reduction methods have been applied for robust speech recognition in terms of speech enhancement. Such methods can be divided according to the number of microphone channels: single-channel approaches using a single microphone and multi-channel approaches based on a microphone array. Methods that have been studied actively in recent years are based on a multi-channel microphone array to correctly estimate noise signals using relevant techniques, e.g., beamforming and spatial filtering [18; 19; 20]. Generally, the microphone array is available for immovable electronic devices in indoor environments, and most are targeted at reducing room reverberation for voice-driven control of smart home devices. In this study, we primarily target outdoor noises that user devices with a single-channel microphone are exposed to. Two methods have been primarily been applied in single-channel noise reduction, i.e., prior knowledge-based and model-based methods. Each has been widely applied in speech recognition tasks in consideration of operating environments [4]. Prior knowledge-based approaches attempt to estimate noise components and use them to eliminate noises in speech regions. Representative approaches include filtering techniques, e.g., spectral subtraction and adaptive comb filtering, and spectrum reconstruction methods, e.g., the minimum mean squared error (MMSE). In recent years, many studies have investigated model-based methods. They train mapping properties between clean speech and noise-contaminated speech using a DNN-based regression model and a generative adversarial network (GAN) and then reconstruct the de-noised speech from noisy input speech via DNN decoding [21; 22]. Although DNN-based modeling approaches have demonstrated stable performance, they have several limitations relative to our target task. First, the distributed speech recognition architecture assigns front-end processes (including feature extraction and voice trigger) to the user device. The device also plays a role in reducing background noises from the input speech. Note that computationally intensive DNN decoding may be a difficult process for the user device. Next, the regression model and GAN should maintain noise characteristics to train the mapping properties between clean and noisy speech data. Thus, they provide very stable performance for stationary noises but may perform inefficiently on non-stationary noises captured by the device when the user moves. Finally, if the model-based noise reduction process is assigned to the UAV system rather than the user device, the overall input speech data should be transferred continuously from the device to the UAV. In this case, the data size increases significantly compared to delivering acoustic feature parameters, thereby increasing the frequency of packet loss. Consequently, this study focuses on prior knowledge-based noise reduction using a single microphone. The most representative method of this technique is spectral subtraction [23; 24]. This technique assumes that noise and speech signals are not correlated and combined additively. Here, if the noise signal characteristics change slowly compared to those of speech signals, the noise components estimated in non-speech regions are used to reduce the noise signals in the speech regions. Let \(x(t)\), \(s(t)\), and \(n(t)\) be noisy speech, clean speech, and additive noise, respectively. The power spectrum of clean speech (\(\left|\hat{S}(\omega)\right|\)) is estimated by subtracting the spectrum of noise signals (\(\left|\hat{N}(\omega)\right|\)) from the spectrum of noisy speech (\(\left|X(\omega)\right|\)) as follows. \[\left|\hat{S}(\omega)\right|=\left\{ \begin{array}{ll}\left|X(\omega)\right|- \left|\hat{N}(\omega)\right|,&\text{if }\left|X(\omega)\right|>\left|\hat{N}(\omega)\right|\\ 0,&\text{otherwise}\end{array} \right.