  With the advent of digital technology, it is more common that committed
crimes or legal disputes involve some form of speech recording where the
identity of a speaker is questioned [1]. In face of this situation, the field
of forensic speaker identification has been looking to shed light on the
problem by quantifying how much a speech recording belongs to a particular
person in relation to a population. In this work, we explore the use of speech
embeddings obtained by training a CNN using the triplet loss. In particular, we
focus on the Spanish language which has not been extensively studies. We
propose extracting the embeddings from speech spectrograms samples, then
explore several configurations of such spectrograms, and finally, quantify the
embeddings quality. We also show some limitations of our data setting which is
predominantly composed by male speakers. At the end, we propose two approaches
to calculate the Likelihood Radio given out speech embeddings and we show that
triplet loss is a good alternative to create speech embeddings for forensic
speaker identification.
