To weight or not to weight: source-normalised LDA for speaker recognition using i-vectors
Mitchell McLaren, David A. van Leeuwen · 2011
Source-normalised Linear Discriminant Analysis (SN-LDA) was recently introduced to improve speaker recognition using i-vectors extracted from multiple speech sources. SN-LDA normalises for the effect of speech source in the calculation of the between-speaker covariance matrix. Sourcenormalised-and-weighted (SNAW) LDA computes a weighted average of source-normalised covariance matrices to better exploit available information. This paper investigates the statistical significance of performance gains offered by SNAW-LDA over SN-LDA. An exhaustive search for optimal scatter weights was conducted to determine the potential benefit of SNAW-LDA. When evaluated on both NIST 2008 and 2010 SRE datasets, scatter-weighting in SNAW-LDA tended to overfit the LDA transform to the evaluation dataset while offering few statistically significant performance improvements over SN-LDA. Index Terms: speaker recognition, linear discriminant analysis, i-vector, source variability