It is well-known that speakers who entrain to one another have more successful conver- sations than those who do not. Previous re- search has shown that interlocutors entrain on linguistic features in both written and spoken monolingual domains. More recent work on code-switched communication has also shown preliminary evidence of entrainment on cer- tain aspects of code-switching (CSW). How- ever, such studies of entrainment in code- switched domains have been extremely few and restricted to human-machine textual inter- actions. Our work studies code-switched spon- taneous speech between humans, finding that (1) patterns of written and spoken entrainment in monolingual settings largely generalize to code-switched settings, and (2) some patterns of entrainment on code-switching in dialogue agent-generated text generalize to spontaneous code-switched speech. Our findings give rise to important implications for the potentially "uni- versal" nature of entrainment as a communica- tion phenomenon, and potential applications in inclusive and interactive speech technology.
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Developing an Integrated Model of Speech Entrainment
Entrainment, the phenomenon of conversational partners’ speech becoming more similar to each other, is generally accepted to be an important aspect of human-human and human-machine communication. However, there is a gap between accepted psycholinguistic models of entrainment and the body of empirical findings, which includes a large number of unexplained negative results. Existing research does not provide insights specific enough to guide the implementation of entraining spoken dialogue systems or the interpretation of entrainment as a measure of quality. A more integrated model of entrainment is proposed, which looks for consistent explanations of entrainment behavior on specific features and how they interact with speaker, session, and utterance characteristics.
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- Award ID(s):
- 1845710
- PAR ID:
- 10213788
- Date Published:
- Journal Name:
- Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
- Page Range / eLocation ID:
- 5159 to 5163
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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