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    • Hattori, Koosuke (3)
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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 3 of 3 1 Unsupervised lexical acquisition of relative spatial concepts using spoken user utterances ... Sagara, Rikunari; Taguchi, Ryo; Taniguchi, Akira. - : Taylor & Francis, 2021 BASE Show details 2 Unsupervised lexical acquisition of relative spatial concepts using spoken user utterances ... Sagara, Rikunari; Taguchi, Ryo; Taniguchi, Akira. - : Taylor & Francis, 2021 BASE Show details 3 Unsupervised Lexical Acquisition of Relative Spatial Concepts Using Spoken User Utterances ... Sagara, Rikunari; Taguchi, Ryo; Taniguchi, Akira; Taniguchi, Tadahiro; Hattori, Koosuke; Hoguro, Masahiro; Umezaki, Taizo. - : arXiv, 2021 Abstract: This paper proposes methods for unsupervised lexical acquisition for relative spatial concepts using spoken user utterances. A robot with a flexible spoken dialog system must be able to acquire linguistic representation and its meaning specific to an environment through interactions with humans as children do. Specifically, relative spatial concepts (e.g., front and right) are widely used in our daily lives, however, it is not obvious which object is a reference object when a robot learns relative spatial concepts. Therefore, we propose methods by which a robot without prior knowledge of words can learn relative spatial concepts. The methods are formulated using a probabilistic model to estimate the proper reference objects and distributions representing concepts simultaneously. The experimental results show that relative spatial concepts and a phoneme sequence representing each concept can be learned under the condition that the robot does not know which located object is the reference object. Additionally, ... : 27 pages, 12 figures, submitted to Advanced Robotics ... Keyword: Artificial Intelligence cs.AI; FOS Computer and information sciences; I.2.9; Robotics cs.RO URL: https://arxiv.org/abs/2106.08574 https://dx.doi.org/10.48550/arxiv.2106.08574 BASE Hide details

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