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

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).”

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