On the use of ideal binary masks for improving phonetic classification

Ideal binary masks are binary patterns that encode the masking characteristics of speech in noise. Recent evidence in speech perception suggests that such binary patterns provide sufficient information for human speech recognition. Motivated by these findings, we propose to use ideal binary masks to improve phonetic modeling. We show that by combining the outputs of classifiers trained on the traditional MFCC features and this novel speech pattern, statistically significant improvements over the baseline MFCC based classifier can be achieved for the task of phonetic classification. Using the combined classifiers, we achieve an error rate of 19.5% on the TIMIT phonetic classification task using multilayer perceptrons as the underlying classifier.