Published papers

米国海洋大気庁の試算によれば，大気汚染の防止により10年間で約9000億円のコスト削減効果がある．一方，大気汚染の代表的指標であるPM2.5の予測精度は現状では十分ではない．そこで，PM2.5の予測問題に対し，Dynamic Pre-trainingを導入したDeep Recurrent Neural Networkを提案する．日本52都市のデータを用いて比較評価を行い，予測精度の向上を確認した．

In this paper, we propose a novel method for a robot to detect robot-directed speech: to distinguish speech that users speak to a robot from speech that users speak to other people or to themselves. The originality of this work is the introduction of a multimodal semantic confidence (MSC) measure, which is used for domain classification of input speech based on the decision on whether the speech can be interpreted as a feasible action under the current physical situation in an object manipulation task. This measure is calculated by integrating speech, object, and motion confidence with weightings that are optimized by logistic regression. Then we integrate this measure with gaze tracking and conduct experiments under conditions of natural human-robot interactions. Experimental results show that the proposed method achieves a high performance of 94% and 96% in average recall and precision rates, respectively, for robot-directed speech detection.

In this paper, we propose a novel method for a robot to detect robot-directed speech from other speech: to distinguish speech that users speak to a robot from speech that users speak to other people or to himself. The originality of this work is the introduction of Multimodal Semantic Confidence measure, which is used for domain classification of input speech based on deciding whether the speech can be interpreted as a feasible action under the current physical situation in an object manipulation task. This measure is calculated by integrating speech, object, and motion confidence with weightings that are optimized by logistic regression. Then we integrate this measure with gaze tracking, and conduct experiments under conditions of natural human-robot interactions. Experimental results show that the proposed method achieved a high performance of 94% and 96% in average recall and precision rates for robot-directed speech detection.