%Aigaion2 BibTeX export from Idiap Publications %Tuesday 10 December 2024 06:31:03 PM @INPROCEEDINGS{Ramet_SLT_2018, author = {Ramet, Gaetan and Garner, Philip N. and Baeriswyl, Michael and Lazaridis, Alexandros}, projects = {SUMMA}, month = dec, title = {CONTEXT-AWARE ATTENTION MECHANISM FOR SPEECH EMOTION RECOGNITION}, booktitle = {IEEE Workshop on Spoken Language Technology}, year = {2018}, pages = {126-131}, location = {Athens, Greece}, isbn = {978-1-5386-4333-4}, url = {http://www.slt2018.org/}, 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 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.}, pdf = {https://publications.idiap.ch/attachments/papers/2018/Ramet_SLT_2018.pdf} }