  The domain of speech emotion recognition (SER) has persistently been a
frontier within the landscape of machine learning. It is an active field that
has been revolutionized in the last few decades and whose implementations are
remarkable in multiple applications that could affect daily life. Consequently,
the Iberian Languages Evaluation Forum (IberLEF) of 2024 held a competitive
challenge to leverage the SER results with a Spanish corpus. This paper
presents the approach followed with the goal of participating in this
competition. The main architecture consists of different pre-trained speech and
text models to extract features from both modalities, utilizing an attention
pooling mechanism. The proposed system has achieved the first position in the
challenge with an 86.69% in Macro F1-Score.
