Deep Neural Baselines for Computational Paralinguistics
Table 2: Evaluation scores for MSE, MAE, and ρ on the development set with different parameter combinations regarding (i) sample rate, (ii) window size, (iii) amount of convolutional blocks. The proposed approach is varied by each one of the three parameters while the others remain fixed. Note that the stride was fixed at 0.1s. Models denoted with * were submitted to the challenge.
['[EMPTY]', 'MSE', 'MAE', '[ITALIC] ρ']
To allow proper comparison of models with (hyper-) parameters, we set the batch size to 64 and the amount of epochs to 8 for training. This allowed testing multiple parameter combinations without large computational and time-consuming overhead. The results imply that our models perform slightly better (ρ≈0.29±0.03) than the strongest baseline models (ρ≈0.26) on the development set. Generally, we found that during training, the loss was monotonically decreasing on the training and the development set. However, most models tend to overfit on the training set after 6 epochs and we therefore saved the best models during the entire training process based on their loss on the development set. However, a combination of techniques is beyond the scope of this work as we aim to provide a generic approach rather than a tailored solution for this specific problem.