Uncertainty propagation in front end factor analysis for noise robust speaker recognition

摘要

In this study, we explore the propagation of uncertainty in the state-of-the-art speaker recognition system. Specifically, we incorporate the uncertainty associated with observation features into the i-Vector extraction framework. To prove the concept, both the oracle and practically estimated uncertainty are used for evaluation. The oracle uncertainty is calculated assuming the knowledge of clean speech features, while the estimated uncertainties are obtained using SPLICE and joint-GMM based methods. We evaluate the proposed framework on both YOHO and NIST 2010 Speaker Recognition Evaluation (SRE) corpora by artificially introducing noise at different SNRs. In the speaker verification experiments, we confirmed that the proposed uncertainty based i-Vector extraction framework shows significant robustness against noise.