In this paper, we report automatic pronunciation assessment experiments at phone-level on a read speech corpus in French, collected from 23 Japanese speakers learning French as a foreign language. We compare the standard approach based on Goodness Of Pronunciation (GOP) scores and phone-specific score thresholds to the use of logistic regressions (LR) models. French native speech corpus, in which artificial pronunciation errors were introduced, was used as training set. Two typical errors of Japanese speakers were considered: /ö/ and /v/ of ten mispronounced as [l] and [b], respectively. The LR classifier achieved a 64.4% accuracy similar to the 63.8% accuracy of the baseline threshold method, when using GOP scores and the expected phone identity as input features only. A significant performance gain of 20.8% relative was obtained by adding phonetic and phonological features as input to the LR model, leading to a 77.1% accuracy. This LR model also outperformed another baseline approach based on linear discriminant models trained on raw f-BANK coefficient features.