# EmoRL: Continuous Acoustic Emotion Classification using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1804.04053 · HTML: https://mlsanity.com/paper/1804.04053/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2018.

## Built on by (most cited first, 4 of 4)

- [A Survey on Deep Reinforcement Learning for Audio-Based Applications](https://mlsanity.com/paper/2101.00240/) (2021)
- [Internally Rewarded Reinforcement Learning](https://mlsanity.com/paper/2302.00270/) (2023)
- [A novel policy for pre-trained Deep Reinforcement Learning for Speech Emotion Recognition](https://mlsanity.com/paper/2101.00738/) (2021)
- [Domain Adapting Deep Reinforcement Learning for Real-world Speech Emotion Recognition](https://mlsanity.com/paper/2207.12248/) (2022)

## Abstract (arXiv)

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.