Towards a Talking Tiny Cognitive Architecture for the Study of Spoken Language Evolution

Understanding spoken language evolution can offer important insight that is needed to devise intelligent systems with speech capabilities. However, existing work mostly focuses on mathematical and software models that are insensitive to naturalistic conditions, even though such conditions are essential in the evolution of real languages. This paper discusses the implementation of Talking Tiny Cognitive Architecture proposed to study language evolution in more naturalistic conditions. The architecture can generate new words that are verbally communicated to describe environment conditions. It also attempts to understand and learn words verbally communicated by another architecture. Experiments describe the performance of the implementation as an embedded system.