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As large-scale deployments of spoken dialog systems in call centers become more common, a wealth of information is gathered about the call center business as well as the operation of these systems from their daily logs. This paper describes the "VoiceTone Daily News" data mining tool for analyzing this information and presenting it in a readily comprehensible and customizable form suitable for use by anyone from system designers to call center businesses. Relevant business and dialog features are extracted from the speech logs of caller-system interactions and tracked by a trend analysis algorithm. They describe novel techniques for generating alerts on multiple data streams while avoiding redundant "Knock-on" alerts. Some initial experiments with automated measures of dialog success are described as possible additional features to track.
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