Impact of Web based language modeling on speech understanding

Data sparseness in building statistical language models for spoken dialog systems is a critical problem. In a previous paper we addressed this issue by exploiting the World Wide Web (WWW) and other external data sources in a financial transaction domain. In this paper, we evaluate the impact of improved speech recognition due to Web-based language model (WebLM) on the speech understanding performance in a new domain. As speech understanding system we use a natural language call-routing system. Experimental results show that the WebLM improves the speech recognition performance by 1.7% to 2.7% across varying amounts of in-domain data. The improvements in action classification (AC) performance were modest yet consistent ranging from 0.3% to 0.8%.