Modeling pronunciation variants is an important topic for automatic speech recognition. This paper investigates the pronunciation modeling at the lexical level, and presents a detailed modeling of the probabilities of the pronunciation variants. The approach is evaluated on the French ESTER2 corpus, and a significant word error rate reduction is achieved through the use of context and speaking rate dependent modeling of these pronunciation probabilities. A rule-based approach makes it possible to derive a priori probabilities for the pronunciation of words that are not present in the training corpus, and a MAP estimation process yields reliable estimates of the pronunciation variant probabilities.