On the Robustness of Speech Emotion Recognition for Human-Robot Interaction with Deep Neural Networks

@article{Lakomkin2018OnTR,
  title={On the Robustness of Speech Emotion Recognition for Human-Robot Interaction with Deep Neural Networks},
  author={Egor Lakomkin and Mohammad-Ali Zamani and Cornelius Weber and Sven Magg and Stefan Wermter},
  journal={2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2018},
  pages={854-860}
}
Speech emotion recognition (SER) is an important aspect of effective human-robot collaboration and received a lot of attention from the research community. For example, many neural network-based architectures were proposed recently and pushed the performance to a new level. However, the applicability of such neural SER models trained only on in-domain data to noisy conditions is currently under-researched. In this work, we evaluate the robustness of state-of-the-art neural acoustic emotion… CONTINUE READING
10
Twitter Mentions

Citations

Publications citing this paper.
SHOWING 1-4 OF 4 CITATIONS

Speech Emotion Recognition Using Deep Learning Techniques: A Review

Ruhul Amin Khalil, Edward Jones, +3 authors Thamer Alhussain
  • IEEE Access
  • 2019
VIEW 18 EXCERPTS
CITES METHODS
HIGHLY INFLUENCED

Analysis of Deep Learning Architectures for Cross-Corpus Speech Emotion Recognition

Jack Parry, Dimitri Palaz, +4 authors Gregor Hofer
  • INTERSPEECH 2019
  • 2019
VIEW 1 EXCERPT
CITES METHODS

Incorporating End-to-End Speech Recognition Models for Sentiment Analysis

  • 2019 International Conference on Robotics and Automation (ICRA)
  • 2019
VIEW 3 EXCERPTS
CITES BACKGROUND & METHODS

References

Publications referenced by this paper.
SHOWING 1-10 OF 31 REFERENCES

Adieu features? End-to-end speech emotion recognition using a deep convolutional recurrent network

  • 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2016
VIEW 1 EXCERPT