Yeah that 's why uh people used these lifters were inverse variance weighting lifters basically that makes uh uh Euclidean distance more like uh Mahalanobis distance with a diagonal covariance when you knew what all the variances were over the old data.
Mm - hmm. Mm - hmm. Hmm.
What they would do is that they would weight each coefficient by inverse of the variance. Turns out that uh the variance decreases at least at fast , I believe , as the index of the cepstral coefficients. I think you can show that uh uh analytically.
Mm - hmm.