EmoRL: Real-time Acoustic Emotion Classification using Deep Reinforcement Learning

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.

@InProceedings{LZWMW18, 
 	 author =  {Lakomkin, Egor and Zamani, Mohammad Ali and Weber, Cornelius and Magg, Sven and Wermter, Stefan},  
 	 title = {EmoRL: Real-time Acoustic Emotion Classification using Deep Reinforcement Learning}, 
 	 booktitle = {Proceedings of the International Conference on Robotics and Automation (ICRA)},
 	 editors = {},
 	 number = {},
 	 volume = {},
 	 pages = {4445--4450},
 	 year = {2018},
 	 month = {May},
 	 publisher = {},
 	 doi = {10.1109/ICRA.2018.8461058}, 
 }