Incrementally learn the relevance of words in a dictionary for spoken language acquisition

This paper discusses a spoken language acquisition system for a command-and-control interface. The proposed system learns a set of words through coupled commands and demonstrations. The user can teach the system a new command by demonstrating the uttered command through an alternative interface. With these coupled commands and demonstrations, the system can learn the acoustic representations of the used words coupled with the meaning or semantics. In previous work the focus was mainly on a batch learning scheme to train the model. All the commands and demonstrations had to be stored and the model had to be retrained from scratch every time a new demonstration was given by the user. This work presents a Bayesian learning scheme where the dictionary of learned words can be updated when new data is presented. The dictionary can automatically expand to add new words or shrink to forget old words. The proposed system is tested on a language acquisition task where the user suddenly starts using new words. The results show that the proposed system can learn the new words quicker than a baseline where the size of the dictionary cannot be adjusted.