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| Presentation | 2023-02-22 13:00 Assessment System of Remote Structured Interview using Bimodal Neural Network Shengzhou Yi (UTokyo), Toshiaki Yamasaki (Talent and Assessment), Toshihiko Yamasaki (UTokyo)  ITS2022-67 IE2022-84 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | A structured interview is a data collection method that relies on asking questions in a set of order to eliminate subjectivity of interviewers. This method is employed in survey research, job interviews, and so on. However, interviews are time- and money consuming for both interviewers and interviewees. Therefore, a demand for artificial intelligence (AI)-assisted structured interview is increasing. For this purpose, in this paper, a bimodal neural network is used to analyze the interview data and predict the assessment labels. The text and audio features of the interviews are extracted by deep neural networks. Furthermore, class-imbalanced learning methods and margin ranking loss are used to improve the model performance. The proposed neural network is trained on a closed dataset that consists of 24,143 interview videos. The interview videos are assigned with the labels of seven assessment criteria to clarify whether the candidates get high or low scores. For the experimental results, the combination of bimodal features, class-imbalanced learning, and margin ranking loss makes the proposed model achieve an average accuracy of 75.89% in the two-class classification. Such AI-supported job interview systems would also give more chances to candidates because they will have a chance to have an online interview in addition to curriculum vitae screening. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Multimodal Recognition / Online Interview / Assessment System / Class Imbalance / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 122, no. 385, IE2022-84, pp. 141-146, Feb. 2023. | |
| Paper # | IE2022-84 | |
| Date of Issue | 2023-02-14 (ITS, IE) | |
| ISSN | Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | ITS2022-67 IE2022-84 |
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| Conference Information | |
| Committee | IE ITS ITE-MMS ITE-ME ITE-AIT |
| Conference Date | 2023-02-21 - 2023-02-22 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Hokkaido Univ. |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Image Processing, etc. |
| Paper Information | |
| Registration To | IE |
| Conference Code | 2023-02-IE-ITS-MMS-ME-AIT |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Assessment System of Remote Structured Interview using Bimodal Neural Network |
| Sub Title (in English) | |
| Keyword(1) | Multimodal Recognition |
| Keyword(2) | Online Interview |
| Keyword(3) | Assessment System |
| Keyword(4) | Class Imbalance |
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| 1st Author's Name | Shengzhou Yi |
| 1st Author's Affiliation | The University of Tokyo (UTokyo) |
| 2nd Author's Name | Toshiaki Yamasaki |
| 2nd Author's Affiliation | Talent and Assessment (Talent and Assessment) |
| 3rd Author's Name | Toshihiko Yamasaki |
| 3rd Author's Affiliation | The University of Tokyo (UTokyo) |
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| Speaker | Author-1 |
| Date Time | 2023-02-22 13:00:00 |
| Presentation Time | 15 minutes |
| Registration for | IE |
| Paper # | ITS2022-67, IE2022-84 |
| Volume (vol) | vol.122 |
| Number (no) | no.384(ITS), no.385(IE) |
| Page | pp.141-146 |
| #Pages | 6 |
| Date of Issue | 2023-02-14 (ITS, IE) |
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