Probabilistic Context-Free Grammars can be used for speech recognition or
syntactic analysis thanks to especially efficient algorithms. In this
paper, we propose an instanciation of such a grammar, whose
mathematical properties are intuitively more suitable for those tasks
than SCFG's (Stochastic CFG), without requiring specific analysis
algorithms. Results on Susanne text show that up to $33\%$ of analysis
errors made by a SCFG can be avoided with this model.