## 5 Conclusions  
In this paper, we have proposed several efficient approaches for voice-driven control of multiple UAVs. First, we proposed an efficient speech recognition scheme based on distributed speech recognition. In this scheme, the user device and UAV system share the recognition processes. Here, the user device processes the front-end module and the UAV system processes the recognition module. In this study, we focused on the front-end module, in which noise reduction and voice trigger functionalities should be carefully handled in consideration of environmental conditions during multi-UAV control.  
To handle non-stationary noises in outdoor environments, we enhanced the conventional MMSE approach in consideration of spectral energy variation. To overcome the drawbacks of the conventional hard-decision parameter in the MMSE technique, we employed the spectral energy variance in each frequency bin to determine speech-present and speech-absent bins. In addition, we proposed a new voice trigger approach in which multiple acoustic models are processed to handle multiple UAVs and three consecutive filtering processes are performed to detect trigger words and disregard non-trigger words.  
To verify the proposed approach, we developed a simulation environment in which a user device controlled multiple UAV systems via a wireless network, and we performed several experiments on speech and noise data. We found that the proposed approaches outperformed the conventional approaches. The proposed noise reduction approach outperformed conventional approaches, including spectral subtraction and adaptive comb filtering, thereby demonstrating reliable performance in a reduction of non-stationary noises, e.g., babble and F16 fighter jet noises. In addition, the proposed voice trigger approach outperformed the conventional approach based on a single word trigger. The proposed multi-channel voice trigger approach provided reliable trigger word detection accuracy for the target trigger words.  
In future work, we plan to investigate efficient approaches for the speech recognition process performed on UAV systems.  
Conceptualization, J.-S.P.; methodology, J.-S.P.; software, J.-S.P. and H.-J.N.; validation, J.-S.P. and H.-[PERSON]; formal analysis, J.-S.P.; writing--original draft preparation, J.-S.P.; writing--review and editing,J.-S.P.; supervision, J.-S.P.; project administration, J.-S.P.; funding acquisition, J.-S.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by Hankuk University of Foreign Studies Research Fund, the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1A2C1013162), the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2020-2016-0-00313) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation).  
**Conflicts of Interest:** The authors declare no conflict of interest.
Figure A4: Communication flow of voice trigger-based multi-UAV control.  
Figure A5: Experimental environments simulating user device and five UAV systems.