Speaker Verification in Emotional Talking Environments based on Third-Order Circular Suprasegmental Hidden Markov Model
This work addresses the problem of reduced speaker verification accuracy in emotional environments for applications like security or voice authentication, but it is incremental as it builds on existing HMM-based approaches.
The paper tackled speaker verification in emotional talking environments by proposing a Third-Order Circular Suprasegmental Hidden Markov Model (CSPHMM3) as a classifier, achieving higher accuracy than state-of-the-art methods like GMM, SVM, and VQ on an Emirati-accented Arabic speech database.
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).