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Improving Speech Emotion Recognition Using Graph Attentive Bi-Directional Gated Recurrent Unit Network
<span title="2020-10-25">2020</span>
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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/trpytsxgozamtbp7emuvz2ypra" style="color: black;">Interspeech 2020</a>
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The manner that human encodes emotion information within an utterance is often complex and could result in a diverse salient acoustic profile that is conditioned on emotion types. In this work, we propose a framework in imposing a graph attention mechanism on gated recurrent unit network (GA-GRU) to improve utterance-based speech emotion recognition (SER). Our proposed GA-GRU combines both long-range time-series based modeling of speech and further integrates complex saliency using a graph
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<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.21437/interspeech.2020-1733">doi:10.21437/interspeech.2020-1733</a>
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... ture. We evaluate our proposed GA-GRU on the IEMOCAP and the MSP-IMPROV database and achieve a 63.8% UAR and 57.47% UAR in a four class emotion recognition task. The GA-GRU obtains consistently better performances as compared to recent state-of-art in per-utterance emotion classification model, and we further observe that different emotion categories would require distinct flexible structures in modeling emotion information in the acoustic data that is beyond conventional left-to-right or vice versa. Index Terms : speech emotion recognition, graph, attention mechanism, recurrent neural network In the following sections, we will briefly introduce the two datasets and the acoustic features used in this work.
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