Name
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2988257.2988268 | AVEC@ACM Multimedia |
Keywords | Field | DocType |
emotion recognition, valence, arousal, bottleneck features, neural networks, regression, speech transcription, word embedding | Modalities,Arousal,Transcription (linguistics),Speech transcription,Computer science,Emotion recognition,Speech recognition,Natural language processing,Artificial intelligence,Word embedding,Artificial neural network,Test set | Conference |
Citations | PageRank | References |
6 | 0.45 | 4 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Filip Povolný | 1 | 7 | 0.82 |
| Petr Schwarz | 2 | 991 | 69.47 |
| Michal Hradis | 3 | 132 | 14.19 |
| Anna Popková | 4 | 7 | 0.80 |
| Lubomir Otrusina | 5 | 12 | 2.27 |
| Pavel Smrž | 6 | 38 | 6.71 |
| Ian Wood | 7 | 9 | 3.21 |
| Cecile Robin | 8 | 6 | 0.45 |
| L. Lamel | 9 | 2135 | 361.63 |