Balancing data-driven and rule-based approaches in the context of a Multimodal Conversational System
Srinivas Bangalore, Michael O. Johnston · 2004
Moderate-sized rule-based spoken language models for recognition and understanding are easy to develop and provide the ability to rapidly prototype conversational applications. However, scalability of such systems is a bottleneck due to the heavy cost of authoring and maintenance of rule sets and inevitable brittleness due to lack of coverage in the rule sets. In contrast, data-driven approaches are robust and the procedure for model building is usually simple. However, the lack of data in a particular application domain limits the ability to build data-driven models. In this paper, we address the issue of combining data-driven and grammar-based models for rapid prototyping of robust speech recognition and understanding models for a multimodal conversational system. We also present methods that reuse data from different domains and investigate the limits of such models in the context of a particular application domain. 1