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.Index Terms-Mutual information, objective measures, speech intelligibility prediction.
Speech intelligibility prediction of noisy and processed noisy speech is important in a number of application domains such as hearing instruments and forensics. Most available objective intelligibility measures employ either a signal-to-noise ratio (SNR)-based or correlation-based comparison between frequency bands of the clean and the processed speech. In this paper, we approach the speech intelligibility prediction from the angle of information theory and show that an information theoretic concept provides a unified viewpoint on both the SNR and the correlation based approaches. Two objective intelligibility measures are introduced based on estimated mutual information between the clean speech and the processed speech in the time and the frequency subband domain. Our proposed measures show high correlation with subjective intelligibility measure (i.e. word correct scores) and comparative results with the short-term objective intelligibility measure (STOI).
In this paper, a novel interference mitigation approach using an autoencoder in combination with a traditional interference detection filter is introduced. It is shown that by employing the gated convolution, the encoder has the ability to learn the signal pattern from the remaining interference-free signal. The decoder can recover the interference-contaminated signal segments from the bottleneck representation as computed by the encoder.Experimental results show that the proposed method can provide a remarkable improvement in signal-to-interference-plus-noise ratio (SINR) and preserves its robustness on real radar measurements in severely disturbed scenarios that are more complex than the training dataset.
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