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Egor Lakomkin presented at IJCNLP 2017 conference, Taipei, Taiwan

On 29th of November, Egor Lakomkin presented at the The 8th International Joint Conference on Natural Language Processing in Taipei, Taiwan

Title: Reusing Neural Speech Representations for Auditory Emotion Recognition


Abstract: Acoustic emotion recognition aims to categorize the affective state of the speaker and is still a difficult task for machine learning models. The difficulties come from the scarcity of training data, general subjectivity in emotion perception resulting in low annotator agreement, and the uncertainty about which features are the most relevant and robust ones for classification. In this paper, we will tackle the latter problem. Inspired by the recent success of transfer learning methods we propose a set of architectures which utilize neural representations inferred by training on large speech databases for the acoustic emotion recognition task. Our experiments on the IEMOCAP dataset show 10% relative improvements in the accuracy and F1-score over the baseline recurrent neural network which is trained endto-end for emotion recognition.