Rapid language model development using external resources for new spoken dialog domains

The paper addresses a critical problem in deploying a spoken dialog system (SDS). One of the main bottlenecks of SDS deployment for a new domain is data sparseness in building a statistical language model. Our goal is to devise an efficient method to build a reliable language model for a new SDS. We consider the worst, yet quite common, scenario where only a small amount (/spl sim/1.7 K utterances) of domain specific data is available for the target domain. We present a new method that exploits external static text resources that are collected for other speech recognition tasks as well as dynamic text resources acquired from the World Wide Web (WWW). We show that language models built using external resources can be used jointly with a limited in-domain (baseline) language model to obtain significant improvements in speech recognition accuracy. Combining language models built using external resources with the in-domain language model provides over 20% reduction in WER over the baseline in-domain language model. Equivalently, we achieve almost the same level of performance by having ten times as much in-domain data (17 K utterances).