Improved speaker recognition using DCT coefficients as features

We recently proposed the use of coefficients extracted from the 2D discrete cosine transform (DCT) of log Mel filter bank energies to improve speaker recognition over the traditional Mel frequency cepstral coefficients (MFCC) with appended deltas and double deltas (MFCC/deltas). Selection of relevant coefficients was shown to be crucial, resulting in the proposal of a zig-zag parsing strategy. While 2D-DCT coefficients provided significant gains over MFCC/deltas, the parsing strategy remains sensitive to the number of filter bank outputs and the analysis window size. In this work, we analyze this sensitivity and propose two new data-driven methods of utilizing DCT coefficients for speaker recognition: rankDCT and pcaDCT. The first, rankDCT, is an automated coefficient selection strategy based on the highest average intra-frame energy rank. The alternate method, pcaDCT, avoids the need for selection and instead projects DCT coefficients to the desired dimensionality via principal component analysis (PCA). All features including MFCC/deltas are tuned on a subset of the PRISM database to subsequently highlight any parameter sensitivities of each feature. Evaluated on the recent NIST SRE'12 corpus, pcaDCT consistently outperforms both rankDCT and zzDCT features and offers an average 20% relative improvement over MFCC/deltas across conditions.