  In this article, we provide a model to estimate a real-valued measure of the
intelligibility of individual speech segments. We trained regression models
based on Convolutional Neural Networks (CNN) for stop consonants
\textipa{/p,t,k,b,d,g/} associated with vowel \textipa{/A/}, to estimate the
corresponding Signal to Noise Ratio (SNR) at which the Consonant-Vowel (CV)
sound becomes intelligible for Normal Hearing (NH) ears. The intelligibility
measure for each sound is called SNR$_{90}$, and is defined to be the SNR level
at which human participants are able to recognize the consonant at least 90\%
correctly, on average, as determined in prior experiments with NH subjects.
Performance of the CNN is compared to a baseline prediction based on automatic
speech recognition (ASR), specifically, a constant offset subtracted from the
SNR at which the ASR becomes capable of correctly labeling the consonant.
Compared to baseline, our models were able to accurately estimate the
SNR$_{90}$~intelligibility measure with less than 2 [dB$^2$] Mean Squared Error
(MSE) on average, while the baseline ASR-defined measure computes
SNR$_{90}$~with a variance of 5.2 to 26.6 [dB$^2$], depending on the consonant.
