تقرير
Multiple output samples per input in a single-output Gaussian process
| العنوان: | Multiple output samples per input in a single-output Gaussian process |
|---|---|
| المؤلفون: | Wong, Jeremy H. M., Zhang, Huayun, Chen, Nancy F. |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language, Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing |
| الوصف: | The standard Gaussian Process (GP) only considers a single output sample per input in the training set. Datasets for subjective tasks, such as spoken language assessment, may be annotated with output labels from multiple human raters per input. This paper proposes to generalise the GP to allow for these multiple output samples in the training set, and thus make use of available output uncertainty information. This differs from a multi-output GP, as all output samples are from the same task here. The output density function is formulated to be the joint likelihood of observing all output samples, and latent variables are not repeated to reduce computation cost. The test set predictions are inferred similarly to a standard GP, with a difference being in the optimised hyper-parameters. This is evaluated on speechocean762, showing that it allows the GP to compute a test set output distribution that is more similar to the collection of reference outputs from the multiple human raters. Comment: This paper is presented in the "Symposium for Celebrating 40 Years of Bayesian Learning in Speech and Language Processing and Beyond", which is a satellite event of the ASRU workshop, on 20 December 2023. https://bayesian40.github.io/ |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2306.02719 |
| رقم الأكسشن: | edsarx.2306.02719 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |