Two adaptation methods for probabilistic context-free grammars are considered. In the first, an LR parser is enhanced with an error-recovery procedure which synthesises production rules in order to allow strings which are not in the language to be accepted. In the second, a hierarchy of probabilistic context-free grammars with progressively weaker structure has the same effect. Both methods are intended to promote flexibility and introduce a learning capability into linguistic modelling for speech recognition.