CONTEXT-AWARE ATTENTION MECHANISM FOR SPEECH EMOTION RECOGNITION
| Type of publication: | Conference paper |
| Citation: | Ramet_SLT_2018 |
| Publication status: | Published |
| Booktitle: | IEEE Workshop on Spoken Language Technology |
| Year: | 2018 |
| Month: | December |
| 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. |
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SUMMA |
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| Added by: | [UNK] |
| Total mark: | 0 |
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