The design of commercial spoken dialog systems is most commonly based on hand-crafting call flows. Voice interaction designers write prompts, predict caller responses, set speech recognition parameters, implement interaction strategies, all based on "best design practices". Recently, we presented the mathematical framework "Contender" (similar to reinforcement learning) that allows for replacing manual decisions made during system design by datadriven soft decisions made at system run time optimizing the cumulative reward of an application. The current paper reports on the results of 26 Contenders implemented in commercial applications processing a total of about 15 million calls.
Bibliographic reference. Suendermann, D. / Liscombe, J. / Bloom, J. / Li, G. / Pieraccini, Roberto (2011): "Large-scale experiments on data-driven design of commercial spoken dialog systems", In INTERSPEECH-2011, 813-816.