Lip-Reading Aids Word Recognition Most in Moderate Noise: A Bayesian Explanation Using High-Dimensional Feature Space
Figure 4
A Bayesian model of speech perception can describe human identification performance.
A vocabulary of size N = 2000 was used. Words were distributed in an irregular manner in a space of dimension n = 40. For details of the fits, see the Supplemental Material. a: Data (symbols) and model fits (lines) for A-alone and AV conditions. The red line is the multisensory enhancement obtained from the model. b: Same for impoverished visual information (AV*). c: Words in high-density regions are harder to recognize. In the simulation in a, words were categorized according to their mean distance to other words. When the mean distance is large (sparse, solid lines), recognition performance in both A-alone and AV conditions is higher than when the mean distance is small (dense, dashed lines).