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Gunrock is the winner of the 2018 Amazon Alexa Prize, as evaluated by coherence and engagement from both real users and Amazonselected expert conversationalists. We focus on understanding complex sentences and having in-depth conversations in open domains. In this paper, we introduce some innovative system designs and related validation analysis. Overall, we found that users produce longer sentences to Gunrock, which are directly related to users' engagement (e.g., ratings, number of turns). Additionally, users' backstory queries about Gunrock are positively correlated to user satisfaction. Finally, we found dialog flows that interleave facts and personal opinions and stories lead to better user satisfaction.

Speech alignment is where talkers subconsciously adopt the speech and language patterns of their interlocutor. Nowadays, people of all ages are speaking with voice-activated, artificially-intelligent (voice-AI) digital assistants through phones or smart speakers. This study examines participants’ age (older adults, 53–81 years old vs. younger adults, 18–39 years old) and gender (female and male) on degree of speech alignment during shadowing of (female and male) human and voice-AI (Apple’s Siri) productions. Degree of alignment was assessed holistically via a perceptual ratings AXB task by a separate group of listeners. Results reveal that older and younger adults display distinct patterns of alignment based on humanness and gender of the human model talkers: older adults displayed greater alignment toward the female human and device voices, while younger adults aligned to a greater extent toward the male human voice. Additionally, there were other gender-mediated differences observed, all of which interacted with model talker category (voice-AI vs. human) or shadower age category (OA vs. YA). Taken together, these results suggest a complex interplay of social dynamics in alignment, which can inform models of speech production both in human-human and human-device interaction.

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This study examined how speaking style and guise influence the intelligibility of text-to-speech (TTS) and naturally produced human voices. Results showed that TTS voices were less intelligible overall. Although using a clear speech style improved intelligibility for both human and TTS voices (using “newscaster” neural TTS), the clear speech effect was stronger for TTS voices. Finally, a visual device guise decreased intelligibility, regardless of voice type. The results suggest that both speaking style and visual guise affect intelligibility of human and TTS voices. Findings are discussed in terms of theories about the role of social information in speech perception.