Human languages have multiple strategies that allow us to discriminate objects in a vast variety of contexts. Colours have been extensively studied from this point of view. In particular, previous research in artificial language evolution has shown how artificial languages may emerge based on specific strategies to distinguish colours. Still, it has not been shown how several strategies of diverse complexity can be autonomously managed by artificial agents . We propose an intrinsic motivation system that allows agents in a population to create a shared artificial language and progressively increase its expressive power. Our results show that with such a system agents successfully regulate their language development, which indicates a relation between population size and consistency in the emergent communicative systems.

This paper presents TexAfon 2.0, an improved version of the text processing tool TexAFon, specially oriented to the generation of synthetic speech with expressive content. TexAFon is a text processing module in Catalan and Spanish for TTS systems, which performs all the typical tasks needed for the generation of synthetic speech from text: sentence detection, pre-processing, phonetic transcription, syllabication, prosodic segmentation and stress prediction. These improvements include a new normalisation module for the standardisation on chat text in Spanish, a module for the detection of the expressed emotions in the input text, and a module for the automatic detection of the intended speech acts, which are briefly described in the paper. The results of the evaluations carried out for each module are also presented.