Abstract
When deploying a spoken dialogue system in a new domain, one faces a situation where little to no data is available to train domain-specific statistical models. We describe our experience with bootstrapping a dialogue system for public transit and weather information in real-word deployment under public use.
We proceeded incrementally, starting from a minimal system put on a toll-free telephone number to collect speech data. We were able to incorporate statistical modules trained on collected data – in-domain speech recognition language models and spoken language understanding – while simultaneously extending the domain, making use of automatically generated semantic annotation.
Our approach shows that a successful system can be built with minimal effort and no in-domain data at hand.