| Abstract |
| This study explored users’ emotional states identified from the wake-up words ―“Hi, KIA!”―using a machine learning algorithm considering the user interface of passenger cars’ voice. We targeted four emotional states, namely, excited, angry, desperate, and neutral, and created a total of 12 emotional scenarios in the context of car driving. Nine college students participated and recorded sentences as guided in the visualized scenario. The wake-up words were extracted from whole sentences, resulting in two data sets. We used the soundgen package and svmRadial method of caret package in open source-based R code to collect acoustic features of the recorded voices and performed machine learning-based analysis to determine the predictability of the modeled algorithm. We compared the accuracy of wake-up words (60.19%: 22%~81%) with that of whole sentences (41.51%) for all nine participants in relation to the four emotional categories. Accuracy and sensitivity performance of individual differences were noticeable, while the selected features were relatively constant. This study provides empirical evidence regarding the potential application of the wake-up words in the practice of emotion-driven user experience in communication between users and the artificial intelligence system. |
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| Key Words |
| Voice-User Interface, VUI, Wake-Up Words, Machine-Learning, Acoustic Feature, svmRadial, Emotional User Scenario, 보이스 인터페이스, 기동어, 기계 학습, 음성 피쳐, 서포트 벡터 머신, 감성 시나리오 |
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