Towards a Methodology Supporting Semiautomatic Annotation of Head Movements in Video-recorded Conversations
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Towards a Methodology Supporting Semiautomatic Annotation of Head Movements in Video-recorded Conversations. / Paggio, Patrizia; Navarretta, Costanza; Jongejan, Bart; Aguirrezabal Zabaleta, Manex.
Proceedings of The Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop. Association for Computational Linguistics, 2021. s. 151-159.Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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TY - GEN
T1 - Towards a Methodology Supporting Semiautomatic Annotation of Head Movements in Video-recorded Conversations
AU - Paggio, Patrizia
AU - Navarretta, Costanza
AU - Jongejan, Bart
AU - Aguirrezabal Zabaleta, Manex
PY - 2021
Y1 - 2021
N2 - We present a method to support the annotation of head movements in video-recorded conversations. Head movement segments from annotated multimodal data are used to train a model to detect head movements in unseen data. The resulting predicted movement sequences are uploaded to the ANVIL tool for post-annotation editing. The automatically identified head movements and the original annotations are compared to assess the overlap between the two. This analysis showed that movement onsets were more easily detected than offsets, and pointed at a number of patterns in the mismatches between original annotations and model predictions that could be dealt with in general terms in post-annotation guidelines.
AB - We present a method to support the annotation of head movements in video-recorded conversations. Head movement segments from annotated multimodal data are used to train a model to detect head movements in unseen data. The resulting predicted movement sequences are uploaded to the ANVIL tool for post-annotation editing. The automatically identified head movements and the original annotations are compared to assess the overlap between the two. This analysis showed that movement onsets were more easily detected than offsets, and pointed at a number of patterns in the mismatches between original annotations and model predictions that could be dealt with in general terms in post-annotation guidelines.
M3 - Article in proceedings
SP - 151
EP - 159
BT - Proceedings of The Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop
PB - Association for Computational Linguistics
ER -
ID: 284176309