• Journal of the Korea Convergence Society
• /
• v.11 no.9
• /
• pp.91-97
• /
• 2020

Use of voice-based interfaces that can interact with users is increasing as AI technology develops. To date, however, most of the research on voice-based interfaces has been technical in nature, focused on areas such as improving the accuracy of speech recognition. Thus, the voice of most voice-based interfaces is uniform and does not provide users with differentiated sensibilities. The purpose of this study is to add a emotional factor suitable for the AI interface. To this end, we have derived emotional factors that should be considered in designing voice interface. In addition, we looked at voice trends that differed from country to country. For this study, we conducted interviews with voice industry experts from Finland and Norway, countries that use their own independent languages.

• The Journal of the Korea Contents Association
• /
• v.22 no.2
• /
• pp.10-21
• /
• 2022

In order to improve formal presentation attitudes such as presentation of job interviews and presentation of project results at the company, there are few automated methods other than observation by colleagues or professors. In previous studies, it was reported that the speaker's stable speech and gaze processing affect the delivery power in the presentation. Also, there are studies that show that proper feedback on one's presentation has the effect of increasing the presenter's ability to present. In this paper, considering the positive aspects of correction, we developed a program that intelligently corrects the wrong presentation habits and attitudes of college students through facial analysis of videos and analyzed the proposed program's performance. The proposed program was developed through web-based verification of the use of redundant words and facial recognition and textualization of the presentation contents. To this end, an artificial intelligence model for classification was developed, and after extracting the video object, facial feature points were recognized based on the coordinates. Then, using 4000 facial data, the performance of the algorithm in this paper was compared and analyzed with the case of facial recognition using a Teachable Machine. Use the program to help presenters by correcting their presentation attitude.