dc.contributor.author | Povolny, Filip | |
dc.contributor.author | Matejka, Pavel | |
dc.contributor.author | Hradis, Michal | |
dc.contributor.author | Popkova, Anna | |
dc.contributor.author | Otrusina, Lubomir | |
dc.contributor.author | Smrz, Pavel | |
dc.contributor.author | Wood, Ian | |
dc.contributor.author | Robin, Cécile | |
dc.contributor.author | Lamel, Lori | |
dc.date.accessioned | 2017-12-11T15:30:20Z | |
dc.date.available | 2017-12-11T15:30:20Z | |
dc.date.issued | 2016-10-16 | |
dc.identifier.citation | Povolny, Filip, Matejka, Pavel, Hradis, Michal, Popkov, Anna, Otrusina, Lubomir, Smrz, PaveL, Wood, Ian, Robin, Cécile, Lamel, Lori. (2016). Multimodal Emotion Recognition for AVEC 2016 Challenge. Paper presented at the Proceedings of the 6th International Workshop on Audio/Visual Emotion Challenge, Amsterdam, The Netherlands. | en_IE |
dc.identifier.isbn | 978-1-4503-4516-3 | |
dc.identifier.uri | http://hdl.handle.net/10379/7036 | |
dc.description.abstract | This paper describes a systems for emotion recognition and its application on the dataset from the AV+EC 2016 Emotion Recognition Challenge. The realized system was produced and submitted to the AV+EC 2016 evaluation, making use of all three modalities (audio, video, and physiological data). Our work primarily focused on features derived from audio. The original audio features were complement with bottleneck features and also text-based emotion recognition which is based on transcribing audio by an automatic speech recognition system and applying resources such as word embedding models and sentiment lexicons. Our multimodal fusion reached CCC=0.855 on dev set for arousal and 0.713 for valence. CCC on test set is 0.719 and 0.596 for arousal and valence respectively. | en_IE |
dc.description.sponsorship | 5. ACKNOWLEDGMENTS
This work has been funded by the European Union’s Horizon
2020 programme under grant agreement No. 644632
MixedEmotions and No. 645523 BISON, and by Technology
Agency of the Czech Republic project No. TA04011311
“MINT”. It was also supported by the Intelligence Advanced
Research Projects Activity (IARPA) via Department of Defense
US Army Research Laboratory contract number W911NF-
12-C-0013. | en_IE |
dc.format | application/pdf | en_IE |
dc.language.iso | en | en_IE |
dc.publisher | ACM | en_IE |
dc.relation.ispartof | PROCEEDINGS OF THE 6TH INTERNATIONAL WORKSHOP ON AUDIO/VISUAL EMOTION CHALLENGE (AVEC'16) | en |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Ireland | |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/3.0/ie/ | |
dc.subject | Emotion recognition | en_IE |
dc.subject | Valence | en_IE |
dc.subject | Arousal | en_IE |
dc.subject | Bottleneck features | en_IE |
dc.subject | Neural networks | en_IE |
dc.subject | Regression | en_IE |
dc.subject | Speech transcription | en_IE |
dc.subject | Word embedding | en_IE |
dc.title | Multimodal emotion recognition for AVEC 2016 challenge | en_IE |
dc.type | Conference Paper | en_IE |
dc.date.updated | 2017-06-23T10:58:41Z | |
dc.identifier.doi | 10.1145/2988257.2988268 | |
dc.local.publishedsource | http://dx.doi.org/10.1145/2988257.2988268 | en_IE |
dc.description.peer-reviewed | peer-reviewed | |
dc.contributor.funder | EU Horizon 2020 | |
dc.internal.rssid | 12787891 | |
dc.local.contact | Ian Wood. Email: ian.wood@nuigalway.ie | |
dc.local.copyrightchecked | No | |
dc.local.version | PUBLISHED | |
nui.item.downloads | 978 | |