Measuring Convergence and Priming in Tutorial Dialog

Experimental research has shown that human users will converge with dialog systems along many dimensions of speech, including those of acoustic/prosodic features and lexical choice. Other results suggest that speech convergence may provide a variety of benefits to spoken dialog systems, such as an improved user model, increased ease of use, improved feelings of intimacy, and increased compliance on the part of the user. These potential benefits to dialog systems of generating or detecting convergence behaviors suggest the need for corpus studies of convergence, in addition to the experimental results. Here, we build on previous work to demonstrate corpus measures of lexical and acoustic/prosodic convergence. We show that these measures successfully distinguish randomized from naturally ordered data, and demonstrate both lexical and acoustic/prosodic priming effects in our corpus of human/human tutoring dialogs.