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    • Taniguchi, Akira (10)
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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 – 10 of 10 1 Unsupervised Multimodal Word Discovery based on Double Articulation Analysis with Co-occurrence cues ... Taniguchi, Akira; Murakami, Hiroaki; Ozaki, Ryo. - : arXiv, 2022 BASE Show details 2 StarGAN-VC+ASR: StarGAN-based Non-Parallel Voice Conversion Regularized by Automatic Speech Recognition ... Sakamoto, Shoki; Taniguchi, Akira; Taniguchi, Tadahiro. - : arXiv, 2021 BASE Show details 3 Unsupervised lexical acquisition of relative spatial concepts using spoken user utterances ... Sagara, Rikunari; Taguchi, Ryo; Taniguchi, Akira. - : Taylor & Francis, 2021 BASE Show details 4 Unsupervised lexical acquisition of relative spatial concepts using spoken user utterances ... Sagara, Rikunari; Taguchi, Ryo; Taniguchi, Akira. - : Taylor & Francis, 2021 BASE Show details 5 Multiagent Multimodal Categorization for Symbol Emergence: Emergent Communication via Interpersonal Cross-modal Inference ... Hagiwara, Yoshinobu; Furukawa, Kazuma; Taniguchi, Akira. - : arXiv, 2021 BASE Show details 6 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 7 Semantic Mapping Based on Spatial Concepts for Grounding Words Related to Places in Daily Environments Katsumata, Yuki; Taniguchi, Akira; Hagiwara, Yoshinobu... In: Front Robot AI (2019) BASE Show details 8 Improved and Scalable Online Learning of Spatial Concepts and Language Models with Mapping ... Taniguchi, Akira; Hagiwara, Yoshinobu; Taniguchi, Tadahiro. - : arXiv, 2018 BASE Show details 9 Online Spatial Concept and Lexical Acquisition with Simultaneous Localization and Mapping ... Taniguchi, Akira; Hagiwara, Yoshinobu; Taniguchi, Tadahiro. - : arXiv, 2017 BASE Show details 10 Spatial Concept Acquisition for a Mobile Robot that Integrates Self-Localization and Unsupervised Word Discovery from Spoken Sentences ... Taniguchi, Akira; Taniguchi, Tadahiro; Inamura, Tetsunari. - : arXiv, 2016 BASE Show details

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