Elements of information theory
Elements of information theory
Time series: data analysis and theory
Time series: data analysis and theory
Discrete Random Signals and Statistical Signal Processing
Discrete Random Signals and Statistical Signal Processing
Prediction of speech intelligibility based on an auditory preprocessing model
Speech Communication
Evaluation of Objective Quality Measures for Speech Enhancement
IEEE Transactions on Audio, Speech, and Language Processing
An Algorithm for Intelligibility Prediction of Time–Frequency Weighted Noisy Speech
IEEE Transactions on Audio, Speech, and Language Processing
Spectral Magnitude Minimum Mean-Square Error Estimation Using Binary and Continuous Gain Functions
IEEE Transactions on Audio, Speech, and Language Processing
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This paper deals with the problem of predicting the average intelligibility of noisy and potentially processed speech signals, as observed by a group of normal hearing listeners. We propose a model which performs this prediction based on the hypothesis that intelligibility is monotonically related to the mutual information between critical-band amplitude envelopes of the clean signal and the corresponding noisy/processed signal. The resulting intelligibility predictor turns out to be a simple function of the mean-square error (mse) that arises when estimating a clean critical-band amplitude using a minimum mean-square error (mmse) estimator based on the noisy/processed amplitude. The proposed model predicts that speech intelligibility cannot be improved by any processing of noisy critical-band amplitudes. Furthermore, the proposed intelligibility predictor performs well ( ρ 0.95) in predicting the intelligibility of speech signals contaminated by additive noise and potentially non-linearly processed using time-frequency weighting.