## 4 Experiments and Discussion
### Validation of Proposed Voice Trigger Approach
#### 4.2.2 Results and Discussion
In contrast to the DET curve, which observes the overall performance tendency of the two errors, the EER provides a more intuitive measure of performance. Table 1 summarizes the performance of three different approaches in terms of EER. As confirmed in the DET results, the conventional approach demonstrated higher EER compared to the proposed approaches. The second decision criterion outperformed the first criterion. The proposed approach with the first decision criterion demonstrated approximately 23% relative improvement over the conventional approach, and the approach based on the second criterion achieved approximately 51% relative improvement. In addition, the second criterion provided approximately 22% relative improvement over the first criterion. This experimental performance was obtained from the results of the test data for all trigger words. Note that it is necessary to observe trigger word detection performance in a sophisticated manner for each trigger word. For this experiment, we counted the number of correct detections of each trigger word and compared that to the actual number of trigger words in the test data. Figure 14 shows the results for the five trigger words. The same number of each trigger word was included in the evaluation dataset. The proposed approach with the second decision criterion successfully detected trigger words at approximately 79% accuracy, while the conventional and the first criterion approaches obtained approximately 68% and 74% detection accuracy, respectively. The proposed approaches outperformed the conventional approach in overall trigger words. The five trigger words demonstrated different trigger word detection performance. For example, \"alpha,\" bravo,\" and \"Charlie\" showed better performance than the other two trigger words. The conventional approach detected only 84 of 132 utterances of \"delta\" and \"echo,\" providing approximately 64% detection accuracy. In contrast, the proposed approaches provided greater accuracy than 70% for all trigger words. This result indicates that the proposed approaches provide reliable trigger word detection performance for the target trigger words, thereby facilitating a multi-channel voice trigger for speech-driven control of multiple UAVs. Next, we compared the performance to that of a DNN-based approach. Here, we trained a standard CNN that performs detection and identification of trigger words while filtering silence and \begin{table}
\begin{tabular}{c c} \hline \hline
**Approach** & **Equal Error Rate** \\ \hline Conventional & 32.1\% \\ Proposed\_DC1 & 26.0\% \\ Proposed\_DC2 & 21.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: EER (%) of trigger word detection. Figure 14: Detection accuracy for respective trigger words. non-keyword utterances. To facilitate a fair evaluation, this experiment was conducted on clean speech data recorded by five participants in a silent environment. Each participant uttered general conversation speech for approximately two hours while randomly uttering each trigger word 100 times. The layer configuration for this operation is shown in Figure 14. Here, a bundle of 13-dimensional MFCCs extracted from the input speech enters the input layer. The data pass through three convolutional and max pooling layers sequentially. Then, the dimension of the weighted values is reduced by a pooling layer using the max pooling approach. Dropout is then performed to exclude some data to reduce overfitting. While selecting hyperparameters, we empirically set the optimal filter size to \(2\times 2\) and changed the number of filters from 64 to 256 while maintaining a dropout rate of 0.5. The outputs of the convolutional layers enter a fully connected layer. Here, three fully connected layers are followed by a final softmax layer in which the input speech data are categorized as one of the five trigger words or disregarded as a non-trigger utterance.