Probabilistic Linear Discriminant Analysis of i-Vector Posterior Distributions

Explore a comprehensive lecture on Probabilistic Linear Discriminant Analysis (PLDA) of i-Vector posterior distributions presented by Sandro Cumani in 2012 at the Center for Language & Speech Processing (CLSP), Johns Hopkins University. Delve into advanced concepts in speaker recognition and machine learning as Cumani discusses the application of PLDA to i-Vector posterior distributions, a crucial technique in modern speech processing. Gain insights into the mathematical foundations, implementation strategies, and practical applications of this method in the field of speech technology and biometrics. Over the course of 37 minutes, learn how PLDA enhances the accuracy and robustness of speaker verification systems by leveraging probabilistic models to analyze i-Vector distributions.