Mutual Information Theory for Adaptive Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Estimation of Mutual Information: A Survey
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Prediction of speech intelligibility based on an auditory preprocessing model
Speech Communication
Minimum Mean-Square Error Estimation of Discrete Fourier Coefficients With Generalized Gamma Priors
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
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We propose a novel method for objective speech intelligibility prediction which can be useful in many application domains such as hearing instruments and forensics. Most objective intelligibility measures available in the literature employ some kind of signal-to-noise ratio (SNR) or a correlation-based comparison between the spectro-temporal representations of clean and processed speech. In this paper, we investigate the speech intelligibility prediction from the viewpoint of information theory and introduce novel objective intelligibility measures based on the estimated mutual information between the temporal envelopes of clean speech and processed speech in the subband domain. Mutual information allows to account for higher order statistics and hence to consider dependencies beyond the conventional second order statistics. Using data from three different listening tests it is shown that the proposed objective intelligibility measures provide promising results for speech intelligibility prediction in different scenarios of speech enhancement where speech is processed by non-linear modification strategies.