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This research presents a model for Voice-Automatic Terminal Information Service which can provide air traffic congestion information to pilots. By counting syllables in the conversations between air traffic controllers and pilots using the Praat Script program, the air traffic density can be analyzed. T-test statistics is used to compare the difference in the number of syllables obtained from the Praat Script program and from human, whereas Pearson correlation is used to test the linear association between the number of aircraft and the number of syllables counted. The results showed that there were no statistically significant differences in the number of syllables from Praat Script and form human count (P = 0.27). In addition, there was a strong positive correlation between the number of aircraft and the number of syllables in the conversation. Finally, this air traffic density information would be combined with meteorological aerodrome reports and converted into voice messages by Google Text-to-Speech. The voice messages were evaluated by users with comprehensibility level of 5.4 (out of 6) and intelligibility of 92.45%.
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