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In this letter, we propose a novel frequency-domain approach to double-talk detection (DTD) based on the Gaussian mixture model (GMM). In contrast to a previous approach based on a simple and heuristic decision rule utilizing time-domain cross-correlations, GMM is applied to a set of feature vectors extracted from the frequency-domain cross-correlation(More)
SUMMARY In this letter, we propose an improved global soft decision for noisy speech enhancement. From an investigation of statistical model-based speech enhancement, it is discovered that a global soft decision has a fundamental drawback at the speech tail regions of speech signals. For that reason, we propose a new solution based on a smoothed likelihood(More)
In this paper, a residual echo cancellation method is proposed that uses an estimation of the minimum mean-square error (MMSE) based on a statistical model of a speech signal and an echo signal. After the suppression of the echo signal based on the adaptive filter, residual echo is further reduced by the proposed MMSE estimator and the results are compared(More)
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