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![Fig. 3: The graphical model representation of HDP-HLM [14]](publication/357925690/figure/fig2/AS:1113801621549056@1642562408358/The-graphical-model-representation-of-HDP-HLM-14_Q320.jpg)
Human infants acquire their verbal lexicon from minimal prior knowledge of language based on the statistical properties of phonological distributions and the co-occurrence of other sensory stimuli. In this study, we propose a novel fully unsupervised learning method discovering speech units by utilizing phonological information as a distributional...
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Music and language are structurally similar. Such structural similarity is often explained by generative processes. This paper describes the recent development of probabilistic generative models (PGMs) for language learning and symbol emergence in robotics. Symbol emergence in robotics aims to develop a robot that can adapt to real-world environments and human linguistic communications and acquire language from sensorimotor information alone (i.e., in an unsupervised manner). This is regarded as a constructive approach to symbol emergence systems. To this end, a series of PGMs have been developed, including those for simultaneous phoneme and word discovery, lexical acquisition, object and spatial concept formation, and the emergence of a symbol system. By extending the models, a symbol emergence system comprising a multi-agent system in which a symbol system emerges is revealed to be modeled using PGMs. In this model, symbol emergence can be regarded as collective predictive coding. This paper expands on this idea by combining the theory that “emotion is based on the predictive coding of interoceptive signals” and “symbol emergence systems”, and describes the possible hypothesis of the emergence of meaning in music.Keywordssymbol emergence systemsprobabilistic generative modelsymbol emergence in roboticsautomatic music compositionlanguage evolution
