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Abstract
The next generation of voice-based user interface technology will enable easy-to-use automation of new and existing communication services. A critical issue is to move away form highly-structured menus to a more natural human-machine paradigm. In recent years, we have developed algorithms which learn to extract meaning from fluent speech via automatic acquisition and exploitation of salient words, phrases and grammar fragments from a corpus. These methods have been previously applied to the “How may I help you? task for automated operator services, in English, Spanish and Japanese. In this paper, we report on a new application of these language acquisition methods to a more complex customer care task. We report on empirical comparisons which quantify the increased linguistic and semantic complexity over the previous domain. Experimental results on call-type classification will be reported for this new corpus of 30K utterances from live customer traffic. This traffic is drawn form both human/human and human/machine interactions.