A spoken language system for automated call routing

We are interested in the problem of understanding fluently spoken language. In particular, we consider people´s responses to the open-ended prompt of “How may I help you?”. We then further restrict the problem to classifying and automatically routing such a call, based on the meaning of the user´s response. Thus, we aim at extracting a relatively small number of semantic actions from the utterances of a very large set of users who are not trained to the system´s capabilities and limitations. In this paper, we describe the main components of our speech understanding system: the large vocabulary recognizer and the language understanding module performing the call-type classification. In particular, we propose automatic algorithms for selecting phrases from a training corpus in order to enhance the prediction power of the standard word n-gram. The phrase language models are integrated into stochastic finite state machines which outperform standard word n-gram language models. From the speech recognizer output we recognize and exploit automatically-acquired salient phrase fragments to make a call-type classification. This system is evaluated on a database of 10 K fluently spoken utterances collected from interactions between users and human agents