Unsupervised Segmentation of time series data with multiple segmentation structure

Abstract

Human infants can learn phonemes and words from continuous speech signals which have a double articulation structure without correct labels. In addition to speech signals, time-series data with multiple articulation structures also exist in our environment, and learning such structures is also important for realizing robots that can autonomously adapt to the environment. To this end, The nonparametric Bayesian double articulation analyzer (NPB-DAA) has been proposed as a method for learning the double articulation structure in an unsupervised manner. However, since this method composed of a two-level hierarchical statistical model, it cannot deal with time-series data with more than two articulation structures. In this paper, we propose a statistical model that can learn time series data with multiple articulation structures. We also present the results of preliminary experiments using speech signal data.