This paper focuses on error detection in Automatic Speech Recognition (ASR) outputs. A neural network architecture is proposed, which is well suited to handle continuous word representations, like word embeddings. In a previous study, the authors explored the use of linguistic word embeddings, and more particularly their combination. In this new study, the use of acoustic word embeddings is explored. Acoustic word embeddings offer the opportunity of an a priori acoustic representation of words that can be compared, in terms of similarity, to an embedded representation of the audio signal.

First, we propose an approach to evaluate the intrinsic performances of acoustic word embeddings in comparison to orthographic representations in order to capture discriminative phonetic information. Since French language is targeted in experiments, a particular focus is made on homophone words. Then, the use of acoustic word embeddings is evaluated for ASR error detection. The proposed approach gets a classification error rate of 7.94% while the previous state-of-the-art CRF-based approach gets a CER of 8.56% on the outputs of the ASR system which won the ETAPE evaluation campaign on speech recognition of French broadcast news.