How Ordinal Are Your Data?

Sadari Jayawardena, Julien Epps, Zhaocheng Huang

Many affective computing datasets are annotated using ordinal scales, as are many other forms of ground truth involving subjectivity, e.g. depression severity. When investigating these datasets, the speech processing community has chosen classification problems in some cases, and regression in others, while ordinal regression may also arguably be the correct approach for some. However, there is currently essentially no guidance on selecting a suitable machine learning and evaluation method. To investigate this problem, this paper proposes a neural network-based framework which can transition between different modelling methods with the help of a novel multi-term loss function. Experiments on synthetic datasets show that the proposed framework is empirically well-behaved and able to correctly identify classification-like, ordinal regression-like and regression-like properties within multidimensional datasets. Application of the proposed framework to six real datasets widely used in affective computing and related fields suggests that more focus should be placed on ordinal regression instead of classifying or predicting, which are the common practices to date.

 DOI: 10.21437/Interspeech.2020-2030

Cite as: Jayawardena, S., Epps, J., Huang, Z. (2020) How Ordinal Are Your Data?. Proc. Interspeech 2020, 1853-1857, DOI: 10.21437/Interspeech.2020-2030.

  author={Sadari Jayawardena and Julien Epps and Zhaocheng Huang},
  title={{How Ordinal Are Your Data?}},
  booktitle={Proc. Interspeech 2020},