This paper addresses the problem of automatic speech recog- nition (ASR) error detection and their use for improving spo- ken language understanding (SLU) systems. In this study, the SLU task consists in automatically extracting, from ASR tran- scriptions, semantic concepts and concept/values pairs in a e.g touristic information system. An approach is proposed for en- riching the set of semantic labels with error specific labels and by using a recently proposed neural approach based on word embeddings to compute well calibrated ASR confidence mea- sures. Experimental results are reported showing that it is possi- ble to decrease significantly the Concept/Value Error Rate with a state of the art system, outperforming previously published re- sults performance on the same experimental data. It also shown that combining an SLU approach based on conditional random fields with a neural encoder/decoder attention based architec- ture, it is possible to effectively identifying confidence islands and uncertain semantic output segments useful for deciding appropriate error handling actions by the dialogue manager strategy.