Abstract
This paper presents a novel approach on text-dependent biometric speaker verification (SV) based on the ensemble of two feature extraction and classification processes using Hidden Markov Models in a Universal Background Model framework (HMM-UBM) and d-Vectors derived from a Deep Learning Network (DNN) structure. Once the individual SV systems are trained, a third classifier is trained/tuned over individual test scores in the same dataset using three different approaches for comparison purposes: Multilayer Perceptron (MLP), Support Vector Machine (SVM) with three different kernels, and a Fuzzy Inference System (FIS). Obtained results over a proprietary speech database in Spanish, indicate an improved performance, providing an Equal Error Rate (EER) within the range of 0.7%–2.54% when classifier ensembles are used, versus an EER of 3.6% and above obtained in average with individual classifiers. Results in detail corresponding to comparison of the several approaches used in this experimental work are further described.
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Atenco-Vazquez, J.C., Moreno-Rodriguez, J.C., Cruz-Vega, I., Gomez-Gil, P., Arechiga, R., Ramirez-Cortes, J.M. (2020). Classifiers Ensemble of HMM and d-Vectors in Biometric Speaker Verification. In: Martínez-Villaseñor, L., Herrera-Alcántara, O., Ponce, H., Castro-Espinoza, F.A. (eds) Advances in Soft Computing. MICAI 2020. Lecture Notes in Computer Science(), vol 12468. Springer, Cham. https://doi.org/10.1007/978-3-030-60884-2_9
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