dc.creator | Brusco, Pablo | |
dc.creator | Vidal, Jazmín | |
dc.creator | Beňuš, Štefan | |
dc.creator | Gravano, Agustin | |
dc.date.accessioned | 2021-09-23T16:13:41Z | |
dc.date.accessioned | 2022-10-15T14:31:44Z | |
dc.date.available | 2021-09-23T16:13:41Z | |
dc.date.available | 2022-10-15T14:31:44Z | |
dc.date.created | 2021-09-23T16:13:41Z | |
dc.date.issued | 2020-12 | |
dc.identifier | Brusco, Pablo; Vidal, Jazmín; Beňuš, Štefan; Gravano, Agustin; A cross-linguistic analysis of the temporal dynamics of turn-taking cues using machine learning as a descriptive tool; Elsevier Science; Speech Communication; 125; 12-2020; 24-40 | |
dc.identifier | 0167-6393 | |
dc.identifier | http://hdl.handle.net/11336/141377 | |
dc.identifier | CONICET Digital | |
dc.identifier | CONICET | |
dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/4397163 | |
dc.description.abstract | In dialogue, speakers produce and perceive acoustic/prosodic turn-taking cues, which are fundamental for negotiating turn exchanges with their interlocutors. However, little of the temporal dynamics and cross-linguistic validity of these cues is known. In this work, we explore a set of acoustic/prosodic cues preceding three turn-transition types (hold, switch and backchannel) in three different languages (Slovak, American English and Argentine Spanish). For this, we use and refine a set of machine learning techniques that enable a finer-grained temporal analysis of such cues, as well as a comparison of their relative explanatory power. Our results suggest that the three languages, despite belonging to distinct linguistic families, share the general usage of a handful of acoustic/prosodic features to signal turn transitions. We conclude that exploiting features such as speech rate, final-word lengthening, the pitch track over the final 200 ms, the intensity track over the final 1000 ms, and noise-to-harmonics ratio (a voice-quality feature) might prove useful for further improving the accuracy of the turn-taking modules found in modern spoken dialogue systems. | |
dc.language | eng | |
dc.publisher | Elsevier Science | |
dc.relation | info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0167639320302727 | |
dc.relation | info:eu-repo/semantics/altIdentifier/doi/https://doi.org/10.1016/j.specom.2020.09.004 | |
dc.rights | https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ | |
dc.rights | info:eu-repo/semantics/restrictedAccess | |
dc.rights | Atribución-NoComercial-CompartirIgual 2.5 Argentina (CC BY-NC-SA 2.5 AR) | |
dc.subject | DIALOGUE | |
dc.subject | ENGLISH | |
dc.subject | MACHINE LEARNING | |
dc.subject | PROSODY | |
dc.subject | SLOVAK | |
dc.subject | SPANISH | |
dc.title | A cross-linguistic analysis of the temporal dynamics of turn-taking cues using machine learning as a descriptive tool | |
dc.type | info:eu-repo/semantics/article | |
dc.type | info:ar-repo/semantics/artículo | |
dc.type | info:eu-repo/semantics/publishedVersion | |