Context-Aware Attention Mechanism for Speech Emotion Recognition
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 network 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 the state-of-the-art models.
WOS:000463141800019
2018
978-1-5386-4334-1
New York
IEEE Workshop on Spoken Language Technology
126
131
REVIEWED
Event name | Event place | Event date |
Athens, Greece | Dec 18-21, 2018 | |