Decision trees are a popular technique for automatic generation of a phonetic transcription for a given word spelling. We investigate different methods of decision tree design to obtain more compact trees and at the same time better grapheme-to-phoneme transcription quality. We evaluate different approaches to decision tree question selection and pruning using one English and two German grapheme-to-phoneme transcription tasks. In particular, we present a method of automatic generation of decision tree questions from the training data that significantly improves decision tree design.