In this work, the RWTH automatic speech recognition systems developed for the third TC-STAR evaluation campaign 2007 are presented. The RWTH systems make systematic use of internal system combination, combining systems with differences in feature extraction, adaptation methods, and training data used. To take advantage of this, novel feature extraction methods were employed; this year saw the introduction of Gammatone features and MLP based phone posterior features. Further improvements were achieved using unsupervised training, and it is notable that these improvements were achieved using a fairly low amount of automatically transcribed data. Also contributing to the improvements over last year was the switch to MPE training, and the introduction of projecting SAT transforms.