Speech Emotion Recognition Using Fourier Parameters

By Kunxia Wang, Ning An, Bing Nan Li, Yanyong Zhang, Lian Li. IEEE Transactions on Affective Computing, 2015

376 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022, methodology): “According to unimodal affect recognition based on text [126,403], audio [178,194,199], visual”
Speech Emotion Recognition Using Deep Convolutional Neural Network and Discriminant Temporal Pyramid Matching (2018, background): “Affective speech features widely used for emotion recognition can be roughly divided into four categories: 1) acoustic features [34], [35], 2) language features, such as lexical information [36], [37], 3) context information, such as subject, gender, culture influences [38], [39], 4) hybrid features”
Databases, features and classifiers for speech emotion recognition: a review (2018, methodology): “Wang et al. (2015) formulated a new model called Fourier parameter model.”
Speech Intention Classification with Multimodal Deep Learning (2017, methodology): “Although one could fine-tune the manually-selected features [21, 22], doing so would be highly laborious compared to automated ConvNet learning.”