MPI vowel-identification models add Gaussian jnd noise scaled from SMT thresholds

Evidence

Next, the MPI model adds noise to the features. The variance of the noise is set based on the individual abilities of a CI user to perceive the features extracted from the electrodogram. In our implementation we used the results of the SMT task to set the noise variance. The obtained jnd from the SMT task was scaled between 0.001 and 0.5. This number was applied as the variance of the Gaussian noise applied in the MPI model and for this reason is termed jnd noise in Fig. 1 . — Dijk, Pim van, Physiology, Psychoacoustics and…, 1823

Categorization

• DEFINE: Describes how the model bridges a psychophysical measure (SMT) to a cognitive outcome (vowel identification) via internal noise variance.
• XREF: Internal/neural noise as the bridge from physiology to perception echoes the reader’s interest in hearing mechanisms.