# Novel Cascaded Gaussian Mixture Model-Deep Neural Network Classifier for Speaker Identification in Emotional Talking Environments

arXiv: https://arxiv.org/abs/1810.04908 · HTML: https://mlsanity.com/paper/1810.04908/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2018.

## Built on by (most cited first, 4 of 4)

- [CASA-Based Speaker Identification Using Cascaded GMM-CNN Classifier in Noisy and Emotional Talking Conditions](https://mlsanity.com/paper/2102.05894/) (2021)
- [Novel Hybrid DNN Approaches for Speaker Verification in Emotional and Stressful Talking Environments](https://mlsanity.com/paper/2112.13353/) (2021)
- [The exploitation of Multiple Feature Extraction Techniques for Speaker Identification in Emotional States under Disguised Voices](https://mlsanity.com/paper/2112.07940/) (2021)
- [Self-Supervised Speech Representations for Cross-Speaker Dysarthria Detection During Awake Craniotomy](https://mlsanity.com/paper/2610.11825/) (2026)

## Abstract (arXiv)

This research is an effort to present an effective approach to enhance text-independent speaker identification performance in emotional talking environments based on novel classifier called cascaded Gaussian Mixture Model-Deep Neural Network (GMM-DNN). Our current work focuses on proposing, implementing and evaluating a new approach for speaker identification in emotional talking environments based on cascaded Gaussian Mixture Model-Deep Neural Network as a classifier. The results point out that the cascaded GMM-DNN classifier improves speaker identification performance at various emotions using two distinct speech databases: Emirati speech database (Arabic United Arab Emirates dataset) and Speech Under Simulated and Actual Stress (SUSAS) English dataset. The proposed classifier outperforms classical classifiers such as Multilayer Perceptron (MLP) and Support Vector Machine (SVM) in each dataset. Speaker identification performance that has been attained based on the cascaded GMM-DNN is similar to that acquired from subjective assessment by human listeners.