The characterization of the relative information content by spectral features for the objective intelligibility assessment of nonlinearly processed speech

The objective intelligibility assessment of nonlinearly enhanced speech is a widely experienced problem. Nonlinear speech enhancement processors operate primarily on the low-level and transient components of speech. As these sections contain important acoustic cues as well as context-constitutive information, they dominate speech intelligibility. For that reason, shorttime intelligibility measures at low-level and transient components are weighted with their contribution to the overall intelligibility. In this report, spectral features are calculated from auditory sub-bands and are utilized to label these sections of high information content. A genetic optimization is performed to adapt the spectral feature measures to the linearly and nonlinearly processed speech material of a listening test. No improvement is found over existing methods of objective speech intelligibility assessment, using short-time intelligibility calculation and level-dependent weighting. Therewith, the reported results contribute to pinpoint practicable solutions to the problem.