This article analyzes the detection of incorrect entries of nonnative speech in the context of foreign language learning. The purpose is to detect and reject incorrect entries (i.e. those for which the speech signal does not correspond at all to the associated text) while being tolerant to the mispronunciations of non-native speech. The proposed approach exploits the comparison between two text-to-speech alignments : one constrained by the text which is being checked, with another one unconstrained, corresponding to a phonetic decoding. Several comparison criteria are described and combined via a logistic regression function. The article analyzes the influence of different settings, such as the impact of non-native pronunciation variants, the impact of learning the decision functions on native or on non-native speech, as well as the impact of combining various comparison criteria. The performance evaluations are conducted both on native and on non-native speech.