Automatic Estimation of Inteligibility Measure for Consonants in Speech
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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 /p,t,k,b,d,g/ associated with vowel /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.
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