  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.
