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arXiv · 1804.04053

EmoRL: Continuous Acoustic Emotion Classification using Deep Reinforcement Learning

Egor Lakomkin·Mohammad Ali Zamani·Cornelius Weber·Sven Magg·Stefan Wermter
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Abstract

Acoustically expressed emotions can make communication with a robot more efficient. Detecting emotions like anger could provide a clue for the robot indicating unsafe/undesired situations. Recently, several deep neural network-based models have been proposed which establish new state-of-the-art results in affective state evaluation. These models typically start processing at the end of each utterance, which not only requires a mechanism to detect the end of an utterance but also makes it difficult to use them in a real-time communication scenario, e.g. human-robot interaction. We propose the EmoRL model that triggers an emotion classification as soon as it gains enough confidence while listening to a person speaking. As a result, we minimize the need for segmenting the audio signal for classification and achieve lower latency as the audio signal is processed incrementally. The method is competitive with the accuracy of a strong baseline model, while allowing much earlier prediction.

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BibTeXRIS

Egor Lakomkin, Mohammad Ali Zamani, Cornelius Weber, Sven Magg, Stefan Wermter. 2018-04-03. EmoRL: Continuous Acoustic Emotion Classification using Deep Reinforcement Learning. https://arxiv.org/abs/1804.04053

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