Multimodal Emotion Recognition for AVEC 2016 Challenge

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F16%3APU122847" target="_blank" >RIV/00216305:26230/16:PU122847 - isvavai.cz</a>

Výsledek na webu

<a href="http://dl.acm.org/citation.cfm?id=2988268" target="_blank" >http://dl.acm.org/citation.cfm?id=2988268</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/2988257.2988268" target="_blank" >10.1145/2988257.2988268</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Multimodal Emotion Recognition for AVEC 2016 Challenge

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Multimodal Emotion Recognition for AVEC 2016 Challenge

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

R - Projekt Ramcoveho programu EK

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

AVEC '16 Proceedings of the 6th International Workshop on Audio/Visual Emotion Challenge

ISBN

978-1-4503-4516-3

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

75-82

Název nakladatele

Association for Computing Machinery

Místo vydání

Amsterdam

Místo konání akce

Amsterdam

Datum konání akce

16. 10. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000390604700011