%Aigaion2 BibTeX export from Idiap Publications
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@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}
}