  In this paper, we propose a novel unsupervised learning method for the
lexical acquisition of words related to places visited by robots, from human
continuous speech signals. We address the problem of learning novel words by a
robot that has no prior knowledge of these words except for a primitive
acoustic model. Further, we propose a method that allows a robot to effectively
use the learned words and their meanings for self-localization tasks. The
proposed method is nonparametric Bayesian spatial concept acquisition method
(SpCoA) that integrates the generative model for self-localization and the
unsupervised word segmentation in uttered sentences via latent variables
related to the spatial concept. We implemented the proposed method SpCoA on
SIGVerse, which is a simulation environment, and TurtleBot2, which is a mobile
robot in a real environment. Further, we conducted experiments for evaluating
the performance of SpCoA. The experimental results showed that SpCoA enabled
the robot to acquire the names of places from speech sentences. They also
revealed that the robot could effectively utilize the acquired spatial concepts
and reduce the uncertainty in self-localization.
