activity
20152026
most citedSymbol Emergence in Robotics: A Survey

155 citations · 765 across the 63 of their papers we have counts for

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Showing 2021Show all

8 papers · 1 filter

cs.AI2021★ 1 cited

Multiagent Multimodal Categorization for Symbol Emergence: Emergent Communication via Interpersonal Cross-modal Inference

Yoshinobu Hagiwara, Kazuma Furukawa, Akira Taniguchi +1

This paper describes a computational model of multiagent multimodal categorization that realizes emergent communication. We clarify whether the computational model can reproduce th…

cs.SD2021★ 5 cited

StarGAN-VC+ASR: StarGAN-based Non-Parallel Voice Conversion Regularized by Automatic Speech Recognition

Shoki Sakamoto, Akira Taniguchi, Tadahiro Taniguchi +1

Preserving the linguistic content of input speech is essential during voice conversion (VC). The star generative adversarial network-based VC method (StarGAN-VC) is a recently deve…

cs.AI2021★ 2 cited

Unsupervised Lexical Acquisition of Relative Spatial Concepts Using Spoken User Utterances

Rikunari Sagara, Ryo Taguchi, Akira Taniguchi +4

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 ab…

cs.SD2021★ 6 cited

StarGAN-based Emotional Voice Conversion for Japanese Phrases

Asuka Moritani, Ryo Ozaki, Shoki Sakamoto +2

This paper shows that StarGAN-VC, a spectral envelope transformation method for non-parallel many-to-many voice conversion (VC), is capable of emotional VC (EVC). Although StarGAN-…

cs.CL2021★ 7 cited

Double Articulation Analyzer with Prosody for Unsupervised Word and Phoneme Discovery

Yasuaki Okuda, Ryo Ozaki, Tadahiro Taniguchi

Infants acquire words and phonemes from unsegmented speech signals using segmentation cues, such as distributional, prosodic, and co-occurrence cues. Many pre-existing computationa…

cs.RO2021★ 2 cited

Hierarchical Bayesian Model for the Transfer of Knowledge on Spatial Concepts based on Multimodal Information

Yoshinobu Hagiwara, Keishiro Taguchi, Satoshi Ishibushi +2

This paper proposes a hierarchical Bayesian model based on spatial concepts that enables a robot to transfer the knowledge of places from experienced environments to a new environm…