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Abstract
A critical component of any business is interacting with their customers, either by human agents or via automated systems. Many of these interactions involve spoken or written language, with associated customer profile data. Current methods for analyzing, searching and acting upon these interactions are labor intensive and often based on small samples or shallow views of the huge volumes of actual data. In this talk I will describe research directed at enabling businesses to browse, prioritize, select and extract information from these large volumes of customer interactions. A key technical issue is that the data is hetereogenous, comprising both speech and associated call/caller data. Experimental evaluation of these methods on AT&T’s ‘How May I Help You?'(sm) spoken dialog system will be presented.