This article proposes a new approach for contextualizing features for speaker recognition through the discrete cosine transform (DCT). Specifically, we apply a 2D-DCT transform on the Mel filterbank outputs to replace the common Mel frequency cepstral coefficients (MFCCs) appended by deltas and double deltas. A thorough comparison of algorithms for delta computation and DCT-based contextualization for speaker recognition is provided and the effect of varying the size of analysis window in each case is considered. Selection of 2D-DCT coefficients using a zig-zag approach permits definition of an arbitrary feature dimension using the most energized coefficients. We show that 60 coefficients computed using our approach outperforms the standard MFCCs appended with double deltas by up to 25% relative on the NIST 2012 speaker recognition evaluation (SRE) corpus in both Cprimary and equal error rate (EER) while additional coefficients increase system robustness to noise.