To Weight or Not to Weight: Source-Normalised LDA for Speaker Recognition Using i-vectors
نویسندگان
چکیده
Source-normalised Linear Discriminant Analysis (SNLDA) was recently introduced to improve speaker recognition using i-vectors extracted from multiple speech sources. SNLDA 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.
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