Effect of feature warping and decorrelation on Mel Filterbank Slope for speaker recognition

Mel Filterbank Slope (MFS) feature has been shown to consistently perform better than the conventional Mel Frequency Cepstral Co-efficients (MFCC) for speaker recognition. In this work, the issues with respect to the feature's robustness to intersession variability and large dimensionality are addressed. Short term feature warping is used to improve the robustness of MFS. This is observed to give an absolute improvement of 1% in EER on NIST 2003 SRE benchmark dataset. Dimensionality reduction on raw MFS features is performed using Discrete Cosine Transform (DCT). Efficient reduction is obtained using DCT with no deterioration in performance. Feature warping along with DCT is observed to give an absolute improvement of 2% in EER. An overall performance improvement of 3.3% is shown when the feature is fused with temporal information from MFCC.