Automatic speech emotion recognition using modulation spectral features
By Siqing Wu, Tiago H. Falk, Wai-Yip Geoffrey Chan. Speech Communication, 2011
395 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Signal Processing
View Paper: https://www.semanticscholar.org/paper/236df308433a65789ca185da5355059d33eb93e4
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Björn Schuller
- Anton Batliner
- Stefan Steidl
- Laurence Y. Devillers
- Shrikanth S. Narayanan
- Felix Burkhardt
- Christian A. Müller
- Aurobinda Routray
In Experimental and Cognitive Psychology
- Björn Schuller
- Anton Batliner
- Stefan Steidl
- Felix Burkhardt
- Shrikanth S. Narayanan
- Laurence Y. Devillers
- Christian A. Müller
- Aurobinda Routray
In Signal Processing
- Björn Schuller
- Anton Batliner
- Stefan Steidl
- Felix Burkhardt
- Shrikanth S. Narayanan
- Laurence Y. Devillers
- Christian A. Müller
- Aurobinda Routray
Citations in the corpus
- Speech Emotion Recognition Using Deep Convolutional Neural Network and Discriminant Temporal Pyramid Matching (2018, background): “In recent years, modulation spectral features [47] from an auditory-inspired long-term spectro-temporal representation, and weighted spectral features”
- Paralinguistics in speech and language - State-of-the-art and the challenge (2013, background): “An interesting alternative is to investigate expert-crafted higher-level features (Mubarak et al., 2006) such as perceptually motivated features (Wu et al., 2011; Mahdhaoui et al., 2010), or features based on pre-classification (Ringeval and Chetouani, 2008).”
- Databases, features and classifiers for speech emotion recognition: a review (2018, methodology): “Wu et al. (2011) computed modulation spectral features and prosodic features respectively with SVM and LDA classifiers.”