We describe four improvements to a prosody SVM system, including a new method based on text- and part-of-speech-constrained prosodic features. The improved system shows remarkably good performance on NIST SRE06 data, reducing the error rate of an MLLR system by as much as 23% after combination. In addition, an N-best system analysis using eight systems reveals that the prosody SVM is the third and second most important system for 1- and 8-side training conditions, respectively - providing more complementary information than other state-of-the-art cepstral systems. We conclude that as cepstral systems continue to improve, it should become only more important to develop systems based on higher-level features.