## 4 Experiments and Discussion
### Discussion on Evaluation Results  
The main purpose of this paper is to improve the performance of the speech recognition front-end for voice-driven control of multiple UAVs. In particular, we concentrated on two fundamental processes of the front-end, i.e., to reduce background noises and to direct a target vehicle among multiple UAVs and switch targets using speech commands.  
In experimental results for the proposed noise reduction approach, our approach showed notable noise reduction performance compared to several conventional approaches, as addressed in Section 4.1.2. In evaluation based on SD, the proposed approach achieved 19%, 25%, and 14% relative improvement over spectral subtraction, adaptive comb filtering and the conventional MMSE, respectively, in 0 dB SNR condition. In SNR-based evaluation, the approach provided further significant performance improvement, achieving 51%, 63%, and 25% relative improvement over each of three conventional approaches in 0 dB SNR condition.  
Next, with regard to the proposed multi-channel voice trigger approach, we evaluated how well the approach detects five trigger words pertinent to five target UAVs, as addressed in Section 4.2.2. Our approach outperformed the conventional approach based on a single trigger word, achieving 51% relative improvement.  
Finally, we investigated the trigger word detection performance for noisy speech to verify the efficiency of the proposed noise reduction along with the trigger detection approach. As demonstrated in Figure 12, the proposed noise reduction approach showed superior trigger detection accuracy  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Number of Parameters** & **Computational Intensity** \\  & **(Training Phase)** & **(Decoding Phase)** \\ \hline
**CNN-based** & 199,936 & 513,536 \\
**Proposed** & 120,192 & 9984 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Computation complexity of trigger word detection approaches.  
compared to the other approaches, yielding 7%, 12%, and 3% relative improvement over spectral subtraction, adaptive comb filtering, and the conventional MMSE, respectively.  
We attempted to compare the performance of the proposed approach to conventional studies. However, there were few studies on trigger word detection handling multiple trigger words, as addressed in Section 3.3. Most studies have focused on the detection of a single trigger word. In addition, we also address noise reduction for trigger word detection in this study.  
A recent study proposed a noise cancellation method for robust trigger detection [34]. This study used a microphone array (multi-microphone) for noise reduction and handled a single trigger word. In contrast, our proposed approach targets more difficult environments in which we attempted to conduct a single microphone-based noise reduction and detect multiple trigger words simultaneously. Nevertheless, we compared the performance of our approach to that in [34]. Among several experiments that we conducted, the trigger word detection performance with noisy speech (Figure 12) could be used because this experiment was performed to investigate the trigger detection performance for a single trigger word (\"alpha\") for noisy speech with the proposed noise reduction method.  
The conventional study [34] reported that the EER ranged from 10% to 20% according to microphone setting for TV noise dataset. As shown in Figure 12, our approach demonstrated similar performance when noise conditions are 5 dB and 10 dB in terms of SNR. This result indicates that the proposed approach demonstrated reliable performance, even though we used further severe noise data recorded in outdoor environments. In particular, our approach requires less amount of computational intensity because the approach employs a simple stochastic model for trigger word detection.