Diarization of telephone conversations using probabilistic linear discriminant analysis

Official URL: http://risc01.sabanciuniv.edu/record=b1606646 (Table of Contents)

Speaker diarization can be summarized as the process of partitioning an audio data into homogeneous segments according to speaker identity. This thesis investigates the application of the probabilistic linear discriminant analysis (PLDA) to speaker diarization of telephone conversations. We introduce a variational Bayes (VB) approach for inference under a PLDA model for modeling segmental i-vectors in speaker diarization. Deterministic annealing (DA) algorithm is employed in order to avoid locally optimal solutions in VB iterations. We compare our proposed system with a well-known system that applies k-means clustering on principal component analysis coe cients of segmental i-vectors. We used summed channel telephone data from the National Institute of Standards and Technology 2008 Speaker Recognition Evaluation as the test set in order to evaluate the performance of the proposed system. We achieve about 20% relative improvement in diarization error rate as compared to the baseline system.