JSAI2018

Presentation information

Oral presentation

General Session » [General Session] 2. Machine Learning

[1N1] [General Session] 2. Machine Learning

Tue. Jun 5, 2018 1:20 PM - 3:00 PM Room N (2F Sakurajima)

座長:原 聡(大阪大学)

1:20 PM - 1:40 PM

[1N1-01] Learning of Relative Spatial Concepts from Utterances based on MCMC sampling

〇Rikunari Sagara1, Zhixiang Gu1, Ryo Taguchi1, Koosuke Hattori2, Masahiro Hoguro2, Taizo Umezaki1,3 (1. Nagoya Institute of Technology, 2. Chubu University, 3. The University of Tokyo)

Keywords:Symbol Emergent Systems, Language Acquisition, Machine Learning

This paper presents a method for learning relative spatial concepts and phoneme sequences which represent spatial concepts and objects from utterances without knowledge of words. First, phoneme sequences recognized by a general speech recognizer are divided into words on the basis of NPYLM. Then, parameters of the relative spatial distributions are estimated from the segmented words and location information by MCMC sampling. In the experiments, the result showed that the parameters were estimated correctly by the proposed method. Moreover, phoneme sequences which represent spatial concepts and objects were learned successfully by the proposed method.