Combining a Gaussian mixture model front end with MFCC parameters
Matt Stuttle, Mark Gales · 2002
Fitting a Gaussian mixture model (GMM) to the smoothed speech spectrum allows an alternative set of features to be extracted from the speech signal. These features have been shown to possess information complementary to the standard MFCC parameterisation. This paper further investigates the use of these GMM features in combination with MFCCs. The extraction and use of a confidence metric to combine GMM features with MFCCs is described. Re- sults using the confidence metric on the WSJ task are presented. Also, GMM features for speech corrupted with additive noise are extracted from data corrupted with coloured addititve noise. Techniques for noise robustness and compensation are investigated for GMM features and the performance is examined on the RM task with additive noise.