| Presentation # | 7 |
| Session: | Detection, Paralinguistics and Coding |
| Location: | Kallirhoe Hall |
| Session Time: | Wednesday, December 19, 13:30 - 15:30 |
| Presentation Time: | Wednesday, December 19, 13:30 - 15:30 |
| Presentation: |
Poster
|
| Topic: |
Emotion recognition from speech: |
| Paper Title: |
CONTEXT-AWARE ATTENTION MECHANISM FOR SPEECH EMOTION RECOGNITION |
| Authors: |
Gaetan Ramet, Ecole Polytechnique Federale de Lausanne, Switzerland; Philip N. Garner, Idiap Research Institute, Switzerland; Michael Baeriswyl, Alexandros Lazaridis, Swisscom, Switzerland |
| Abstract: |
In this work, we study the use of attention mechanisms to enhance the performance of the state-of-the-art deep learning model in Speech Emotion Recognition (SER). We introduce a new Long Short-Term Memory (LSTM)-based neural attention model which is able to take into account the temporal information in speech during the computation of the attention vector. The proposed LSTM-based model is evaluated on the IEMOCAP dataset using a 5-fold cross-validation scheme and achieved 68.8% weighted accuracy on 4 classes, which outperforms other state-of-the-art models. |