This letter presents a method for data-driven user-specific gait speed estimation for people with Spinal Cord Injuries (SCIs) walking in lower-limb exoskeletons. The scarcity of training data for this population is addressed by leveraging common patterns across users that relate gait changes to speed changes. To bootstrap the process, widely available walking data from uninjured individuals was used as a base dataset. The distribution of this data was first transformed to match smaller user-specific training sets from walking trials of subjects with SCIs. User-specific trials were then selected based on the mutual information between gait speed and features for the combined dataset. The resulting selected data was finally used to build a model for estimating the user's intended gait speed. The performance of this approach was evaluated using data from two users with SCIs walking in an EksoGT exoskeleton with a walker or crutches. Estimation trials were compared when using the base data alone versus when providing personalization via the addition of novel data. The average successful estimation of speed-up and slow-down changes increased from 52% to 67% with personalization using only 8 to 12 steps' worth of user-specific data, with a best-case improvement of 32%, from 48% to 80%. Overall, the proposed method uses the mutual information between gait features and speed to provide a reliable alternative to manual data selection while pooling data from healthy and injured individuals.