Language acquisition through a human-robot interface by combining speech, visual, and behavioral information

Authors:
Naoto Iwahashi
Affiliations:
Sony Computer Science Labs Inc., Takanawa Muse Bldg., 3-14-13 Higashigotanda Shinagawa-ku, Tokyo 141-0022, Japan
Venue:
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Spoken language analysis, modeling and recognition-statistical and adaptive connectionist approaches
Year:
2003

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Abstract

This paper describes new language-processing methods suitable for human-robot interfaces. These methods enable a robot to learn linguistic knowledge from scratch in unsupervised ways. The learning is done through statistical optimization in the process of human-robot communication, combining speech, visual, and behavioral information in a probabilistic framework. The linguistic knowledge learned includes speech units like phonemes, lexicon, and grammar, and is represented by a graphical model that includes hidden Markov models. In experiments, a robot was eventually able to understand utterances according to given situations, and act appropriately.