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Automatic Estimation of Intelligibility Measure for Consonants in Speech
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, 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~intelligibility measure with less than 2 [dB] Mean Squared Error
(MSE) on average, while the baseline ASR-defined measure computes
SNR~with a variance of 5.2 to 26.6 [dB], depending on the consonant.Comment: 5 pages, 1 figure, 7 tables, submitted to Inter Speech 2020
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