This paper presents a study on the modeling of automatic speech recognition errors at the sentence level. We aim in this study to compensate certain phenomena highlighted by the analysis of outputs generated by an ASR error detection system we pre- viously proposed. We investigated three different approaches, that are based respectively on the use of sentence embeddings dedicated to ASR error detection task, on a probabilistic con- textual model, and on a bidirectional long short-term memory (BLSTM) architecture. An approach to build task-specific sen- tence embeddings is proposed and compared to the Doc2vec approach. Experiments are performed on transcriptions gen- erated by the LIUM ASR system applied to the French ETAPE corpus. They show that the proposed sentence embeddings ded- icated to ASR error detection achieve better results than generic sentence embeddings, and that the integration of task-specific embeddings in our system achieves better results than the prob- abilistic contextual model and BLSTM models.