Large pre-trained models are essential in paralinguistic systems, demonstrating effectiveness in tasks like emotion recognition and stuttering detection. In this paper, we employ large pre-trained models for the ACM Multimedia Computational Paralinguistics Challenge, addressing the Requests and Emotion Share tasks. We explore audio-only and hybrid solutions leveraging audio and text modalities. Our empirical results consistently show the superiority of the hybrid approaches over the audio-only models. Moreover, we introduce a Bayesian layer as an alternative to the standard linear output layer. The multimodal fusion approach achieves an 85.4% UAR on HC-Requests and 60.2% on HC-Complaints. The ensemble model for the Emotion Share task yields the best rho value of .614. The Bayesian wav2vec2 approach, explored in this study, allows us to easily build ensembles, at the cost of fine-tuning only one model. Moreover, we can have usable confidence values instead of the usual overconfident posterior probabilities.

翻译：大型预训练模型在副语言系统中至关重要，在情感识别、口吃检测等任务中展现出显著成效。本文针对ACM多媒体计算副语言学挑战赛中的请求与情感共享任务，采用大型预训练模型进行攻关。我们探索了纯音频方案与融合音频与文本模态的混合方案。实证结果一致表明，混合方法在性能上优于纯音频模型。此外，我们引入贝叶斯层替代标准线性输出层。多模态融合方法在HC-Requests数据集上达到85.4%的UAR，在HC-Complaints数据集上达到60.2%的UAR。针对情感共享任务的集成模型取得了最佳rho值0.614。本研究探索的贝叶斯wav2vec2方法，仅需微调单一模型即可轻松构建集成系统。更重要的是，我们能获得可用的置信度数值，而非通常过于自信的后验概率。