Unsupervised phoneme and word discovery method utilizing co-occurrence of object and phonological information

Abstract

In language acquisition, it is known that infants are able to acquire phonemes and words by using statistical cues contained between speeches. In addition, it has been pointed out that infants also utilize co-occurrence with objects in the environment. In this study, we propose an unsupervised phoneme and word discovery method that utilizes the co-occurrence of phonological information and object information. The proposed method is based on Nonparametric Bayesian Double Articulation Analyzer (NPB-DAA), which is a phoneme and word discovery method from phonological features, and Multimodal Latent Dirichlet Allocation (MLDA), which is an object categorization method for multimodal information obtained from objects. We evaluate the effect of using co-occurrence cues for the discovery of words representing objects.