3 papers
eess.AS2026
Bounds on Agreement between Subjective and Objective Measurements
Jaden Pieper, Stephen D. Voran
Objective estimators of multimedia quality are often judged by comparing estimates with subjective "truth data," most often via Pearson correlation coefficient (PCC) or mean-square…
eess.AS2026
Unseen but not Unknown: Using Dataset Concealment to Robustly Evaluate Speech Quality Estimation Models
Jaden Pieper, Stephen D. Voran
We introduce Dataset Concealment (DSC), a rigorous new procedure for evaluating and interpreting objective speech quality estimation models. DSC quantifies and decomposes the perfo…
eess.AS2024
AlignNet: Learning dataset score alignment functions to enable better training of speech quality estimators
Jaden Pieper, Stephen D. Voran
We develop two complementary advances for training no-reference (NR) speech quality estimators with independent datasets. Multi-dataset finetuning (MDF) pretrains an NR estimator o…