Deep Learning as Feature Encoding for Emotion Recognition

10/30/2018
by   Bhalaji Nagarajan, et al.
0

Deep learning is popular as an end-to-end framework extracting the prominent features and performing the classification also. In this paper, we extensively investigate deep networks as an alternate to feature encoding technique of low level descriptors for emotion recognition on the benchmark EmoDB dataset. Fusion performance with such obtained encoded features with other available features is also investigated. Highest performance to date in the literature is observed.

READ FULL TEXT

Authors

page 1

page 2

page 3

page 4

10/31/2018

Deep Net Features for Complex Emotion Recognition

This paper investigates the influence of different acoustic features, au...
04/14/2021

Unsupervised low-rank representations for speech emotion recognition

We examine the use of linear and non-linear dimensionality reduction alg...
04/03/2017

Spatiotemporal Networks for Video Emotion Recognition

Our experiment adapts several popular deep learning methods as well as s...
04/06/2021

Efficient emotion recognition using hyperdimensional computing with combinatorial channel encoding and cellular automata

In this paper, a hardware-optimized approach to emotion recognition base...
10/25/2018

Multi-Channel Auto-Encoder for Speech Emotion Recognition

Inferring emotion status from users' queries plays an important role to ...
07/26/2021

Towards Unbiased Visual Emotion Recognition via Causal Intervention

Although much progress has been made in visual emotion recognition, rese...
08/23/2017

Capturing Long-term Temporal Dependencies with Convolutional Networks for Continuous Emotion Recognition

The goal of continuous emotion recognition is to assign an emotion value...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.