SEMANTIC INFORMATION PROCESSING OF SPOKEN LANGUAGE

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 from 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 lor 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 10K utterances from live customer traffic.