Audio, PCA & noise reduction

Hi,

I am implementing an audio pattern recognition application, the
pattern recognition is done using persons correlation coefficient on a
set of N dimensional feature vectors.

I have considered usage of PCA (Principal components analysis) for
dimensionality reduction ( as done in some speech 2 text
applications ).

PCA take the most dominant dimensions of a collection of N dimensional
vectors ( a matrix ), then, it take the most dominant Y<N dimensions
and express the data only in those Y dimensions ( this is done using
covariance matrix and  Eigen vectors transformations ).

Taking a noisy environment in mind, noise can cause the variance of
one dimension ( out of the N dimensions used ) to become more dominant
and hence, make the most dominant dimensions returned by PCA result
different then if PCA was performed in a non noisy environment, saying
that, it seems usage of PCA is trading performance ( smaller
dimensionality ) for quality ( less resistant to noise ).

Does my assumptions are true? Does usage of PCA with Audio analysis is
sensitive to noisy environments? If so, what other algorithms can be
used to reduce noise ( other then low pass filter )?

Any help would be appreciated.

Thanks,
    NTGO