This paper describes the Intelligent Voice (IV) speaker diarization system for IberSPEECH-RTVE 2018 speaker diarization challenge. We developed a new speaker diarization built on the success of deep neural network based speaker embeddings in speaker verification systems. In contrary to acoustic features such as MFCCs, deep neural network embeddings are much better at discerning speaker identities especially for speech acquired without constraint on recording equipment and environment. We perform spectral clustering on our proposed CNNLSTM-based speaker embeddings to find homogeneous segments and generate speaker log likelihood for each frame. A HMM is then used to refine the speaker posterior probabilities through limiting the probability of switching between speakers when changing frames. We present results obtained on the development set (dev2) as well as the evaluation set …