  This work addresses the problem of speech separation in the Spanish Language
using pre-trained deep learning models. As with many speech processing tasks,
large databases in other languages different from English are scarce. Therefore
this work explores different training strategies using the Conv-TasNet model as
a benchmark. A scale-invariant signal distortion ratio (SI-SDR) metric value of
9.9 dB was achieved for the best training strategy. Then, experimentally, we
identified an inverse relationship between the speakers' similarity and the
model's performance, so an improved ConvTasNet architecture was proposed. The
enhanced Conv-TasNet model uses pre-trained speech embeddings to add a
between-speakers cosine similarity term in the cost function, yielding an
SI-SDR of 10.6 dB. Lastly, final experiments regarding real-time deployment
show some drawbacks in the speakers' channel synchronization due to the need to
process small speech segments where only one of the speakers appears.
