# Speaker Verification in Emotional Talking Environments based on Third-Order Circular Suprasegmental Hidden Markov Model

arXiv: https://arxiv.org/abs/1909.13244 · HTML: https://mlsanity.com/paper/1909.13244/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2019.

## Built on by (most cited first, 1 of 1)

- [Novel Hybrid DNN Approaches for Speaker Verification in Emotional and Stressful Talking Environments](https://mlsanity.com/paper/2112.13353/) (2021)

## Abstract (arXiv)

Speaker verification accuracy in emotional talking environments is not high as it is in neutral ones. This work aims at accepting or rejecting the claimed speaker using his/her voice in emotional environments based on the Third-Order Circular Suprasegmental Hidden Markov Model (CSPHMM3) as a classifier. An Emirati-accented (Arabic) speech database with Mel-Frequency Cepstral Coefficients as the extracted features has been used to evaluate our work. Our results demonstrate that speaker verification accuracy based on CSPHMM3 is greater than that based on the state-of-the-art classifiers and models such as Gaussian Mixture Model (GMM), Support Vector Machine (SVM), and Vector Quantization (VQ).