## 5 Varying Noise Level--Word Frequency Results  
In this section, the experiment results were analysed based on the individual word frequency rather than the speech command phrase as performed in Section 4. There are a total of twelve (12) unique words that make up the twelve (12) UAV speech command phrases. Similar to the speech command phrase analysis in the previous section, the total number of each of the 12 words (contained in the 12 speech command phrases) that were successfully recognised were collated across the 37 participants. For each word, a frequency map and a trendline plot was generated as presented in Figures 9-11. The frequency map is a plot of the total number of times (zero, one, two, three, four, and five) each word was recorded across all participants for each noise level. For example, Figure 9a shows the observation for the first speech word (SW1) 'Go' which appeared a total of four times across all 12 speech command phrases. At 55 dB, of the twenty-three participants whose data were successfully captured and processed, twenty-one had all 4 of the 4 'Go' word instances successful, one had 3 out of 4 successes, and another one had 2 out of 4 successes. At 60 dB, 4 out of 4 'Go' word results were successfully recorded for twenty-five participants, 3 out of 4 for five participants, 2 out of 4 for two participants, 1 out of 4 for two participants, and 0 out of 4 for one participant. At 65 dB, of the 36 participants successfully captured and processed, the 4 of 4 'Go' word recognition was successful for 21 participants, 3 out of 4 for three participants, 2 out of 4 for four participants, 1 out of 4 for two participants, and 0 out of 4 for six participants. At 70 dB, the 4 of 4 'Go' word recognition was thirteen times, 3 out of 4 was one time, 2 out of 4 was one time, 1 out of 4 was five times, and 0 out of 4 was sixteen times. At 75 dB, the 4 of 4 'Go' word recognition was seven times, 3 out of 4 was three times, 2 out of 4 was one time, 1 out of 4 was five times, and 0 out of 4 was sixteen times. At 80 dB, the 4 of 4 'Go' word recognition was two times, 3 out of 4 was two times, 2 out of 4 was five times, 1 out of 4 was two times, and 0 out of 4 was twenty-five times. At 85 dB, the 4 of 4 'Go' word recognition was zero, 3 out of 4 was zero, 2 out of 4 was zero, 1 out of 4 was zero, and 0 out of 4 was thirty-six times. From this result, a decrease can be observed in the total number of times each word was recognised as the noise level was increased from 55 dB to 85 dB. This was represented by the trendline shown in Figure 9b, which was computed using a similar equation to the weighted trendline Equation (1) in the speech command analysis,  
\[y(x)=\frac{\sum_{i=0}^{n}d_{i}(x)f_{i}(x)}{\sum_{i=0}^{n}f_{i}(x)}, \tag{18}\]  
where \(n\) is the total number of each word presented in the 12 speech commands combined, as used in the experiment. Note that \(n\) is different for each word, for example \(n=4\) for SW1 'Go', \(n=2\) for SW2 'Forward', \(n=1\) for SW8 'Land', \(n=3\) for SW10 'One', and \(n=5\) for SW11 'Metre'. \(d_{i}(x)\) is a coefficient less than or equal to \(n\) specifying the number of recognition out of \(n\) word repetitions in a total set of 12 speech commands, for the given \(x\) dB noise level. Note that this corresponds to the \(i\)th value. \(f_{i}(x)\) is the frequency (number of times) of the \(d_{i}(x)\) of \(n\) recognition for the given word at the given \(x\) dB noise level, as indicated on the frequency map. For example, the points on the trendline for the first speech word (SW1), 'Go' was computed and yielded the following values: \(y(55)=3.8696\), \(y(60)=3.4571\), \(y(65)=2.8611\), \(y(70)=1.7222\), \(y(75)=1.1667\), \(y(80)=0.7222\), and \(y(85)=0\). Figure 9c,d shows the frequency map and trendline of the second speech word (SW2), 'Forward', which appeared only two times in the 12 speech command set.