Speaker identification and verification has received a great deal of attention from the speech community, and significant gains in robustness and accuracy have been obtained over the past decade , . However, the features used for identification are still primarily representations of overall spectral characteristics, and thus the models are primarily phonetic in nature, differentiating speakers based on overall pronunciation patterns. This creates difficulties in terms of the amount of enrollment data and complexity of the models required to cover the phonetic space, especially in tasks such as cross-lingual verification where enrollment and testing data may not have similar phonetic coverage. This paper introduces the use of a new feature for speaker verification, residual phase cepstral coefficients (RPCC), to capture speaker characteristics from their vocal excitation patterns. Results on a cross-lingual speaker verification task taken from the NIST 2004 SRE demonstrate that these RPCC features are significantly more accurate than traditional melfrequency cepstral coefficients (MFCC) when the amount of enrollment data available for training is limited. Additionally, because of the significant differences in the nature of the features, combining MFCC and RPCC features shows an improvement in verification results over MFCCs alone.