## 3 Vehicle-Embedded Speech Recognition Front-End for Multi-UAV Control
### Procedures of Vehicle-Embedded Speech Recognition Front-End  
Figure 4 shows the general procedure of the speech recognition front-end. The front-end comprising four main processes: voice activity detection, feature extraction, noise reduction, and voice trigger. The first two processes are necessarily required for speech recognition, whereas noise reduction or voice trigger processes can be skipped according to the system environments and speech recognition purposes.  
The voice activity detection (VAD) process attempts to detect speech regions to select the target speech data for the speech recognition process. Many studies have investigated VAD [11; 12; 13; 14; 15; 16; 17]. Energy-based and zero-crossing rate (ZCR) approaches have been widely used [11; 12; 13; 14]. Energy-based VAD considers a general tendency of speech signals whereby the signal energy of speech regions is greater than that of non-speech regions. Thus, this approach detects speech regions by comparing the energy of a given region to a predetermined threshold to differentiate speech and non-speech regions. The ZCR demonstrates a rate of change between positive and negative values in audio signals. This approach considers a general tendency of speech signals where the ZCR of speech regions is greater than that of non-speech regions.  
Most VAD approaches introduced in recent years have mainly concentrated on DNN techniques, including CNN, CLDNN, and LSTM-RNN [15; 16; 17]. DNN-based VAD approaches are categorized by the types of input features. General approaches use several acoustic features as the DNN input and determine speech or non-speech regions according to DNN procedures. Other approaches use data-driven features, e.g., spectrogram features, and some studies utilize context information of speech data. Although the approaches significantly outperformed simple conventional algorithms, e.g., the energy-based approach, DNN-based approaches incur high computational costs that cannot be processed efficiently on user devices.  
Figure 4: General procedure of speech recognition front-end.  
Figure 3: Proposed distributed speech recognition scheme for multi-UAV control.  
The main purpose of a front-end module in a standard speech recognition system is the extraction of acoustic features from the input speech data that represent acoustic characteristics in the time or frequency domains. The features are extracted for a frame, which means a fixed number of speech signals. Thus, the input speech signals are first divided into frame units of equal duration (e.g., 20 or 30 ms), and features are then extracted for each frame. Such features include (but are not limited to) fundamental frequency, energy, and formant frequencies. The most representative acoustic feature for speech recognition is the Mel-frequency cepstral coefficients (MFCC), which describe the spectral characteristics of speech signals.  
Feature extraction is followed by the noise reduction and voice trigger processes. In this study, we handle these two processes carefully to achieve reliable performance in multi-UAV environments. The proposed noise reduction and voice trigger approaches are described in Sections 3.2 and 3.3, respectively.