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

  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},
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
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Publications referenced by this paper.
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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

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