  In this paper, we demonstrated the benefit of using pre-trained model to
extract acoustic embedding to jointly predict (multitask learning) three tasks:
emotion, age, and native country. The pre-trained model was trained with
wav2vec 2.0 large robust model on the speech emotion corpus. The emotion and
age tasks were regression problems, while country prediction was a
classification task. A single harmonic mean from three metrics was used to
evaluate the performance of multitask learning. The classifier was a linear
network with two independent layers and shared layers, including the output
layers. This study explores multitask learning on different acoustic features
(including the acoustic embedding extracted from a model trained on an
affective speech dataset), seed numbers, batch sizes, and normalizations for
predicting paralinguistic information from speech.
