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

TL;DR

This paper proposes a new third-order circular suprasegmental hidden Markov model (CSPHMM3) for speaker verification in emotional talking environments, showing improved accuracy over existing classifiers using an Arabic speech database.

Contribution

Introduction of CSPHMM3 as a novel classifier that outperforms traditional models like GMM, SVM, and VQ in emotional speaker verification tasks.

Findings

CSPHMM3 achieves higher verification accuracy than state-of-the-art classifiers.

The model performs well on an Emirati-accented Arabic speech database.

Results demonstrate the effectiveness of CSPHMM3 in emotional environments.

Abstract

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).