Jeremy Wong, H. M., Zhang, H., & Chen, N. F. (2023, December 16). Variational Gaussian Process Data Uncertainty. 2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU). https://doi.org/10.1109/asru57964.2023.10389678
Abstract:
A Gaussian process (GP) computes an explainable distributional uncertainty by hypothesising higher output uncertainty for query inputs far from the training inputs. However, a GP may not capture data uncertainty well. Accurate data uncertainty estimation may be important for subjective tasks, such as Spoken Language Assessment (SLA), where human expert raters may disagree on the output scores. This paper shows that a variational approximation of a GP has capacity to learn data uncertainty from the training data. However, standard training criteria tune only a scalar noise hyper-parameter toward the standard deviation of the output reference, thereby limiting the learning of this uncertainty. A training criterion is proposed to explicitly encourage the GP posterior to emulate the distribution of scores from multiple raters. Experiments on the speechocean762 SLA task show that this allows the GP to better express data uncertainty and improves the modelling of inter-rater disagreements.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the AI3 - EVA
Grant Reference no. : SC20/21-816400