The Scalable Version of Probabilistic Linear Discriminant Analysis and Its Potential as A Classifier for Audio Signal Classification
Yuechi Jiang, Frank H. F. Leung · 2018
Probabilistic Linear Discriminant Analysis (PLDA) has exhibited good performance in face recognition and speaker verification. However, it is not widely used as a general-purpose classifier. The major limitation of PLDA lies in that, in the original formulation, the modeling part and the prediction part require the inversion of large matrices, whose sizes are proportional to the number of training vectors in a class. The original formulation of PLDA is not scalable if there are many training vectors, because the matrices will become too large to be inverted. In the literature, some scalable versions for the modeling part have been proposed. In this paper, we propose the scalable version for the prediction part, which completes the scalable version of PLDA. This makes PLDA able to handle a large number of training data, enabling PLDA to be used as a general-purpose classifier for different classification tasks. We then apply PLDA as the classifier to three different audio signal classification tasks, and compare its performance with Support Vector Machine (SVM), which is a widely used general-purpose classifier. Experimental results show that PLDA performs very well and can be even better than SVM, in terms of classification accuracy.