Speaker Recognition with Random Digit Strings Using Uncertainty Normalized HMM-based i-vectors
TABLE VI: Comparison between LDA with and without length normalization.
['Method', '[BOLD] Male EER [%]', '[BOLD] Male NDCFminold', '[BOLD] Male NDCFminnew', '[BOLD] Female EER [%]', '[BOLD] Female NDCFminold', '[BOLD] Female NDCFminnew']
It is generally agreed that applying length normalization before LDA improves its performance. In order to reexamine its positive effect we perform an experiment to compare the performance of length normalization followed by LDA and LDA without length normalization. The system with MFCC features and uncertainty normalization is used as the single system in this experiment. that although cosine similarity scoring applies length normalization implicitly, applying length normalization before LDA and after uncertainty compensation is beneficial. As discussed above, length normalization makes vectors more normally distributed, which is in line with the Gaussian assumptions of LDA.