Rapid and inexpensive development of speech action classifiers for natural language call routing systems

Abstract

Natural language call routing systems are an attractive alternative to interactive voice response systems and directed dialog systems for automating customer service functions. However, the up-front development cost of these systems is an obstacle to their widespread adoption. Much of the cost is associated with the collection and annotation of development data that are used in initial system construction. In this work, we show how the statistical language model and action classifier needed for speech action classification can be developed for a customer's call routing application using no development data. On live data, our approach has comparable performance to a model trained using 100k utterances of in-domain development data. Furthermore, our approach handles the "unknown" class more robustly. These promising experimental results indicate that our method can be used to rapidly and inexpensively deploy call routing systems. ©2010 IEEE.