ABSTRACT
The articulation index (AI) theory serves to predict word recognition scores when
the number of speech cues have been reduced by noise or lack of audibility. It fails
to predict how poorly some subjects do in noise, even when all speech cues have been
made audible with amplification. Such subjects require an unusually large signal-to-noise
ratio (SNR) for a given performance level, and are said to have a large SNR loss.
We have found that the AI can be generalized to predict word recognition scores in
the case of missing (speech cue) dots. Some speech cues appear to be lost on the way
to the brain even though they were audible. Corliss[1] suggested the term channel capacity to describe this phenomenon, and we adopt that term for our use. In this article,
the substantial psychoacoustic and physiological evidence in favor of this generalized
AI is described. Perhaps the strongest evidence is (1) the SNR loss of subjects is
poorly predicted by the degree of their audiometric loss, and (2) their wideband word-recognition
performance in noise can be predicted from their channel capacity inferred from filtered
speech experiments.
KEYWORD
Articulation Index - signal-to-noise ratio loss - channel capacity - missing dots
- audibility
