Making Confident Speaker Verification Decisions With Minimal Speech

摘要

Proposed is an approach to estimating confidence measures on the verification score produced by a Gaussian mixture model (GMM)-based automatic speaker verification system with applications to drastically reducing the typical data requirements for producing a confident verification decision. The confidence measures are based on estimating the distribution of the observed frame scores. The confidence estimation procedure is also extended to produce robust results with very limited and highly correlated frame scores as well as in the presence of score normalization. The proposed Early Verification Decision method utilizes the developed confidence measures in a sequential hypothesis testing framework, demonstrating that as little as 2-10 s of speech on average was able to produce verification results approaching that of using an average of over 100 s of speech on the 2005 NIST SRE protocol.