  Emotion Recognition (ER), Gender Recognition (GR), and Age Estimation (AE)
constitute paralinguistic tasks that rely not on the spoken content but
primarily on speech characteristics such as pitch and tone. While previous
research has made significant strides in developing models for each task
individually, there has been comparatively less emphasis on concurrently
learning these tasks, despite their inherent interconnectedness. As such in
this demonstration, we present PERSONA, an application for predicting ER, GR,
and AE with a single model in the backend. One notable point is we show that
representations from speaker recognition pre-trained model (PTM) is better
suited for such a multi-task learning format than the state-of-the-art (SOTA)
self-supervised (SSL) PTM by carrying out a comparative study. Our methodology
obviates the need for deploying separate models for each task and can
potentially conserve resources and time during the training and deployment
phases.
