# Domain Adapting Deep Reinforcement Learning for Real-world Speech Emotion Recognition

arXiv: https://arxiv.org/abs/2207.12248 · HTML: https://mlsanity.com/paper/2207.12248/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 87 references that are in the map.
- First posted on arXiv 7 Jul 2022.

## Builds on (most cited first, 25 of 25)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey](https://mlsanity.com/paper/2009.13303/) (2020)
- [Overcoming Exploration in Reinforcement Learning with Demonstrations](https://mlsanity.com/paper/1709.10089/) (2017)
- [Sim-to-Real: Learning Agile Locomotion For Quadruped Robots](https://mlsanity.com/paper/1804.10332/) (2018)
- [Transfer Learning in Deep Reinforcement Learning: A Survey](https://mlsanity.com/paper/2009.07888/) (2020)
- [Emotional Voice Conversion: Theory, Databases and ESD](https://mlsanity.com/paper/2105.14762/) (2021)
- [Meta Reinforcement Learning for Sim-to-real Domain Adaptation](https://mlsanity.com/paper/1909.12906/) (2019)
- [Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trends](https://mlsanity.com/paper/2001.00378/) (2020)
- [Domain Adaptation via Teacher-Student Learning for End-to-End Speech Recognition](https://mlsanity.com/paper/2001.01798/) (2020)
- [Improving Cross-Corpus Speech Emotion Recognition with Adversarial Discriminative Domain Generalization (ADDoG)](https://mlsanity.com/paper/1903.12094/) (2019)
- [A Survey on Deep Reinforcement Learning for Audio-Based Applications](https://mlsanity.com/paper/2101.00240/) (2021)
- [Feature Learning in Deep Neural Networks - Studies on Speech Recognition Tasks](https://mlsanity.com/paper/1301.3605/) (2013)
- [Direct Modelling of Speech Emotion from Raw Speech](https://mlsanity.com/paper/1904.03833/) (2019)
- [Unsupervised Adversarial Domain Adaptation for Cross-Lingual Speech Emotion Recognition](https://mlsanity.com/paper/1907.06083/) (2019)
- [Unsupervised Domain Adaptation Schemes for Building ASR in Low-resource Languages](https://mlsanity.com/paper/2109.05494/) (2021)
- [A cross-corpus study on speech emotion recognition](https://mlsanity.com/paper/2207.02104/) (2022)
- [Emotion Recognition for Healthcare Surveillance Systems Using Neural Networks: A Survey](https://mlsanity.com/paper/2107.05989/) (2021)
- [Domain Adaptation for Reinforcement Learning on the Atari](https://mlsanity.com/paper/1812.07452/) (2018)
- [EmoRL: Continuous Acoustic Emotion Classification using Deep Reinforcement Learning](https://mlsanity.com/paper/1804.04053/) (2018)
- [Pre-training in Deep Reinforcement Learning for Automatic Speech Recognition](https://mlsanity.com/paper/1910.11256/) (2019)
- [Deep Reinforcement Learning with Pre-training for Time-efficient Training of Automatic Speech Recognition](https://mlsanity.com/paper/2005.11172/) (2020)
- [A novel policy for pre-trained Deep Reinforcement Learning for Speech Emotion Recognition](https://mlsanity.com/paper/2101.00738/) (2021)
- [Unsupervised Speech Domain Adaptation Based on Disentangled Representation Learning for Robust Speech Recognition](https://mlsanity.com/paper/1904.06086/) (2019)

## Abstract (arXiv)

Computers can understand and then engage with people in an emotionally intelligent way thanks to speech-emotion recognition (SER). However, the performance of SER in cross-corpus and real-world live data feed scenarios can be significantly improved. The inability to adapt an existing model to a new domain is one of the shortcomings of SER methods. To address this challenge, researchers have developed domain adaptation techniques that transfer knowledge learnt by a model across the domain. Although existing domain adaptation techniques have improved performances across domains, they can be improved to adapt to a real-world live data feed situation where a model can self-tune while deployed. In this paper, we present a deep reinforcement learning-based strategy (RL-DA) for adapting a pre-trained model to a real-world live data feed setting while interacting with the environment and collecting continual feedback. RL-DA is evaluated on SER tasks, including cross-corpus and cross-language domain adaption schema. Evaluation results show that in a live data feed setting, RL-DA outperforms a baseline strategy by 11% and 14% in cross-corpus and cross-language scenarios, respectively.