  Recently, the usage of speech self-supervised models (SSL) for downstream
tasks has been drawing a lot of attention. While large pre-trained models
commonly outperform smaller models trained from scratch, questions regarding
the optimal fine-tuning strategies remain prevalent. In this paper, we explore
the fine-tuning strategies of the WavLM Large model for the speech emotion
recognition task on the MSP Podcast Corpus. More specifically, we perform a
series of experiments focusing on using gender and semantic information from
utterances. We then sum up our findings and describe the final model we used
for submission to Speech Emotion Recognition Challenge 2024.
