  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, we show that two processes in the proposed method improve
the estimation accuracy of the concepts: generating candidate word sequences by
class n-gram and selecting word sequences using location information.
Furthermore, we show that clues to reference objects improve accuracy even
though the number of candidate reference objects increases.
