Combining Long Short-Term Memory and Dynamic Bayesian Networks for Incremental Emotion-Sensitive Artificial Listening

@article{Wllmer2010CombiningLS,
  title={Combining Long Short-Term Memory and Dynamic Bayesian Networks for Incremental Emotion-Sensitive Artificial Listening},
  author={Martin W{\"o}llmer and Bj{\"o}rn W. Schuller and Florian Eyben and Gerhard Rigoll},
  journal={IEEE Journal of Selected Topics in Signal Processing},
  year={2010},
  volume={4},
  pages={867-881}
}
The automatic estimation of human affect from the speech signal is an important step towards making virtual agents more natural and human-like. In this paper, we present a novel technique for incremental recognition of the user's emotional state as it is applied in a sensitive artificial listener (SAL) system designed for socially competent human-machine communication. Our method is capable of using acoustic, linguistic, as well as long-range contextual information in order to continuously… CONTINUE READING
Highly Cited
This paper has 148 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 90 extracted citations

Affective rating ranking based on face images in arousal-valence dimensional space

Frontiers of Information Technology & Electronic Engineering • 2018
View 4 Excerpts
Highly Influenced

Attitude Estimation of Unmanned Aerial Vehicle Based on LSTM Neural Network

2018 International Joint Conference on Neural Networks (IJCNN) • 2018
View 1 Excerpt

149 Citations

02040'11'13'15'17'19
Citations per Year
Semantic Scholar estimates that this publication has 149 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 92 references

Bidirectional recurrent neural networks

IEEE Trans. Signal Processing • 1997
View 7 Excerpts
Highly Influenced

Long Short-Term Memory

Neural Computation • 1997
View 8 Excerpts
Highly Influenced

Emotion recognition from speech: Putting ASR in the loop

2009 IEEE International Conference on Acoustics, Speech and Signal Processing • 2009

Similar Papers

Loading similar papers…