  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.
