Combination of Rule-Based and Data-Driven Fusion Methodologies for Different Speaker Verification Modes of Operation

摘要

In this paper we present three methodologies for the fusion of different speaker verification modes of operation. Specifically, we investigate a knowledge-based (rule-based) method, based on biometrics and security knowledge, a data-driven method, based on machine learning fusion models and a combination of them. The experimental results indicate that the hybrid fusion architecture, which is the combination of knowledge-based and data-driven based fusion, offers both robustness against spoofing and improvement in speaker verification performance.