Spoken language acquisition based on reinforcement learning and word unit segmentation

The process of spoken-language acquisition has been one of the topics of greatest interest to linguists for decades. By utilizing modern machine learning techniques, we simulated this process on computers, which helps to understand it and develop new possibilities of applying this concept on intelligent robots, among other things. This paper proposes a new framework for simulating spoken-language acquisition by combining reinforcement learning and unsupervised learning methods. Our experiments also show that a spoken language can be acquired considerably faster by identifying potential word segments from collected ambient sounds in an unsupervised manner.