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.

最近，人们对语音自我监督模型（SSL）在下游任务中的使用越来越关注。在大规模预训练模型通常优于从头开始训练的较小模型的情况下，人们对最佳微调策略的问题仍然很普遍。本文中，我们探讨了 WavLM Large 模型在 MSP Podcast Corpus 的语音情感识别任务中的微调策略。具体而言，我们进行了一系列实验，重点研究了来自话语中的性别和语义信息的使用。然后，我们总结了我们的发现，并描述了我们用于提交 Speech Emotion Recognition Challenge 2024 的最终模型。