| 研究課題/領域番号 |
21K17779
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| 研究種目 |
若手研究
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| 配分区分 | 基金 |
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| 審査区分 |
小区分61020:ヒューマンインタフェースおよびインタラクション関連
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| 研究機関 | 東京大学 |
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研究代表者 |
Zhang Xinlei 東京大学, 大学院情報学環・学際情報学府, 特任研究員 (60898138)
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| 研究期間 (年度) |
2021-04-01 – 2022-03-31
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| 研究課題ステータス |
中途終了 (2021年度)
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| 配分額 *注記 |
4,680千円 (直接経費: 3,600千円、間接経費: 1,080千円)
2023年度: 910千円 (直接経費: 700千円、間接経費: 210千円)
2022年度: 1,430千円 (直接経費: 1,100千円、間接経費: 330千円)
2021年度: 2,340千円 (直接経費: 1,800千円、間接経費: 540千円)
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| キーワード | Tutoring Agent / Speech Recognition / Multi-Modal Interface / Language Learning / Device Wakeup |
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| 研究開始時の研究の概要 |
This research aims to develop a one-of-its-kind language tutoring agent that is fully-automated, adaptive, and user-configurable. To achieve this goal, I plan to 1) develop an architecture to allow users to generate and customize the agent through simple text editing. 2) Develop a technology to chunk the speech template for difficulty adjustments. 3) Evaluate such agents' usability and learning outputs in self-studies. The agent can serve as a complementary assistant to help language tutors train students, or serve as a personalized language tutor to teach every single student in self study.
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| 研究実績の概要 |
During the six months of conducting this project, I mainly achieved two goals: 1) Developed a prototype system to allow users to generate and customize the agent through simple text editing. The system takes a text file containing the transcript of the template speech and the user's feedback mode, then creates the tutoring agent accordingly for adaptive and personalized tutoring. It is part of a paper published in EICS 2021 now.
2) Developed a novel way to allow users to awake the device by changing the prosody when speaking the keyword (e.g., Alexa) for accurate device activations. Evaluation studies show significant advantages of this method compared to Keyword Spotting based method. The results are summarized into a top-conference paper which is under review.
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