This document describes the three systems submitted by the AuDIaS-UAM team for the Albayzin 2018 IberSPEECH-RTVE speaker diarization evaluation. Two of our systems (primary and contrastive 1 submissions) are based on embeddings which are a fixed length representation of a given audio segment obtained from a deep neural network (DNN) trained for speaker classification. The third system (contrastive 2) uses the classical i-vector as representation of the audio segments. The resulting embeddings or i-vectors are then grouped using Agglomerative Hierarchical Clustering (AHC) in order to obtain the diarization labels. The new DNN-embedding approach for speaker diarization has obtained a remarkable performance over the Albayzin development dataset, similar to the performance achieved with the well-known i-vector approach.