# CASA-Based Speaker Identification Using Cascaded GMM-CNN Classifier in Noisy and Emotional Talking Conditions

arXiv: https://arxiv.org/abs/2102.05894 · HTML: https://mlsanity.com/paper/2102.05894/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 51 references that are in the map.
- First posted on arXiv 11 Feb 2021.

## Builds on (most cited first, 6 of 6)

- [Empirical Evaluation of Rectified Activations in Convolutional Network](https://mlsanity.com/paper/1505.00853/) (2015)
- [Novel Cascaded Gaussian Mixture Model-Deep Neural Network Classifier for Speaker Identification in Emotional Talking Environments](https://mlsanity.com/paper/1810.04908/) (2018)
- [Employing Emotion Cues to Verify Speakers in Emotional Talking Environments](https://mlsanity.com/paper/1707.00137/) (2017)
- [Employing both Gender and Emotion Cues to Enhance Speaker Identification Performance in Emotional Talking Environments](https://mlsanity.com/paper/1706.09760/) (2017)
- [Employing Second-Order Circular Suprasegmental Hidden Markov Models to Enhance Speaker Identification Performance in Shouted Talking Environments](https://mlsanity.com/paper/1706.09722/) (2017)
- [Emirati-Accented Speaker Identification in each of Neutral and Shouted Talking Environments](https://mlsanity.com/paper/1804.00981/) (2018)

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

- [Novel Dual-Channel Long Short-Term Memory Compressed Capsule Networks for Emotion Recognition](https://mlsanity.com/paper/2112.13350/) (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)
- [COVID-19 Electrocardiograms Classification using CNN Models](https://mlsanity.com/paper/2112.08931/) (2021)

## Abstract (arXiv)

This work aims at intensifying text-independent speaker identification performance in real application situations such as noisy and emotional talking conditions. This is achieved by incorporating two different modules: a Computational Auditory Scene Analysis CASA based pre-processing module for noise reduction and cascaded Gaussian Mixture Model Convolutional Neural Network GMM-CNN classifier for speaker identification followed by emotion recognition. This research proposes and evaluates a novel algorithm to improve the accuracy of speaker identification in emotional and highly-noise susceptible conditions. Experiments demonstrate that the proposed model yields promising results in comparison with other classifiers when Speech Under Simulated and Actual Stress SUSAS database, Emirati Speech Database ESD, the Ryerson Audio-Visual Database of Emotional Speech and Song RAVDESS database and the Fluent Speech Commands database are used in a noisy environment.