Iterative Statistical Language Model Generation for Use with an Agent-Oriented Natural Language Interface

Abstract

We describe a method for developing a statistical language model (SLM) with high keyword spotting accuracy for a natural language interface (NLI). The NLI is based on the Adaptive Agent Oriented Software Architecture (AAOSA). Our experience shows that this method provides for rapid development of an SLM that is well suited to the requirements of the agent-oriented NLI. Experiment results show a comparatively low equal error rate of 13.2pct for a vocabulary of 2400 keywords. This result is a robust free-form speech-based NLI with a high task completion rate.