We present an end-to-end voice-based conversational agent that is
\nable to engage in naturalistic multi-turn dialogue and align with
\nthe interlocutor\u2019s conversational style. The system uses a series of
\ndeep neural network components for speech recognition, dialogue
\ngeneration, prosodic analysis and speech synthesis to generate
\nlanguage and prosodic expression with qualities that match those
\nof the user. We conducted a user study (N=30) in which participants
\ntalked with the agent for 15 to 20 minutes, resulting in over 8
\nhours of natural interaction data. Users with high consideration
\nconversational styles reported the agent to be more trustworthy
\nwhen it matched their conversational style. Whereas, users with
\nhigh involvement conversational styles were indifferent. Finally,
\nwe provide design guidelines for multi-turn dialogue interactions
\nusing conversational style adaptation.<\/p>\n","protected":false},"excerpt":{"rendered":"
We present an end-to-end voice-based conversational agent that is able to engage in naturalistic multi-turn dialogue and align with the interlocutor\u2019s conversational style. The system uses a series of deep neural network components for speech recognition, dialogue generation, prosodic analysis and speech synthesis to generate language and prosodic expression with qualities that match those of […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"ACM","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"ACM","msr_pages_string":"","msr_page_range_start":"111","msr_page_range_end":"118","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"IVA 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