Automatic speech recognition(ASR) offers the ability to access the semantic content present in spoken language within audio and video documents. While acoustic models based on deep neural networks have recently significantly improved the performances of ASR sys- tems, automatic transcriptions still contain errors. Errors perturb the exploitation of these ASR outputs by introducing noise to the text. To reduce this noise, it is possible to apply an ASR error detection in order to remove recognized words labelled as errors. This paper presents an approach that reaches very good results, better than previous state-of-the-art approaches. This work is based on a neural approach, and more especially on a study targeted to acoustic and linguistic word embeddings, that are representations of words in a continuous space. In comparison to the previous state-of-the-art approach which were based on Conditional Random Fields, our approach reduces the classification error rate by 7.2%.