Kamper et al.<cite> [14]</cite> compared several types of acoustic word embeddings for a word discrimination task related to query-by-example search, finding that embeddings based on convolutional neural networks (CNNs) trained with a contrastive loss outperformed the reference vector approach of Levin et al. [12] as well as several other CNN and DNN embeddings and DTW using several feature types.