Speaker recognition using PCA-based feature transformation

Abstract

This paper introduces a Weighted-Correlation Principal Component Analysis (WCR-PCA) for efficient transformation of speech features in speaker recognition. A Recurrent Neural Network (RNN) technique is also introduced to perform the weighted PCA. The weights are taken as the log-likelihood values from a fitted Single Gaussian-Background Model (SG-BM). For speech features, we show that there are large differences between feature variances which makes covariance based PCA less optimal. A comparative study of the performance of speaker recognition is presented using weighted and unweighted correlation and covariance based PCA. Extensions to improve the extraction of MFCC and LPCC features of speech are also proposed. These are Odd Even filter banks MFCC (OE-MFCC) and Multitaper-Fitted LPCC. The methodologies are evaluated for the i-vector speaker recognition system. A subset of the 2010 NIST speaker recognition evaluation set is used in the performance testing in addition to evaluations on the VoxCeleb1 dataset. A relative improvement of 44% in terms of EER is found in the system performance using the NIST data and 18% using the VoxCeleb1 dataset.