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Attentive Convolutional Neural Network Based Speech Emotion Recognition: A Study on the Impact of Input Features, Signal Length, and Acted Speech
2017
Interspeech 2017
unpublished
Speech emotion recognition is an important and challenging task in the realm of human-computer interaction. Prior work proposed a variety of models and feature sets for training a system. In this work, we conduct extensive experiments using an attentive convolutional neural network with multi-view learning objective function. We compare system performance using different lengths of the input signal, different types of acoustic features and different types of emotion speech
doi:10.21437/interspeech.2017-917
fatcat:vvaoy3lyy5bphjmlhjbaegch4q