# Integrating Recurrence Dynamics for Speech Emotion Recognition

arXiv: https://arxiv.org/abs/1811.04133 · HTML: https://mlsanity.com/paper/1811.04133/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2018.

## Built on by (most cited first, 3 of 3)

- [A Comprehensive Survey on Multi-modal Conversational Emotion Recognition with Deep Learning](https://mlsanity.com/paper/2312.05735/) (2023)
- [Unsupervised low-rank representations for speech emotion recognition](https://mlsanity.com/paper/2104.07072/) (2021)
- [Speech Emotion Recognition Using Deep Sparse Auto-Encoder Extreme Learning Machine with a New Weighting Scheme and Spectro-Temporal Features Along with Classical Feature Selection and A New Quantum-Inspired Dimension Reduction Method](https://mlsanity.com/paper/2111.07094/) (2021)

## Abstract (arXiv)

We investigate the performance of features that can capture nonlinear recurrence dynamics embedded in the speech signal for the task of Speech Emotion Recognition (SER). Reconstruction of the phase space of each speech frame and the computation of its respective Recurrence Plot (RP) reveals complex structures which can be measured by performing Recurrence Quantification Analysis (RQA). These measures are aggregated by using statistical functionals over segment and utterance periods. We report SER results for the proposed feature set on three databases using different classification methods. When fusing the proposed features with traditional feature sets, we show an improvement in unweighted accuracy of up to 5.7% and 10.7% on Speaker-Dependent (SD) and Speaker-Independent (SI) SER tasks, respectively, over the baseline. Following a segment-based approach we demonstrate state-of-the-art performance on IEMOCAP using a Bidirectional Recurrent Neural Network.