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• W2562562196 abstract "The nonlinear distortion in the cepstral coefficients domain introduced by additive noise in the speech signal, results in high degradation performance in systems of Automatic Speech Recognition (ASR). For this reason, we propose a median filter which smooths the probability distribution functions of degraded features, thus reducing the mismatch between training data and test. The new proposal uses a histogram mapping to obtain the PDFs (probability distribution functions) of each feature vector and applies a nonlinear median filtering before mapping to the reference PDF. The algorithm efficiency is analyzed and compared to a recently proposed linear mean filtering technique on the PDFs. From the experimental results it can be concluded that the histogram smoothing through the median nonlinear filtering reduces the mismatch between training data and test, improving the system performance under adverse conditions." @default.
• W2562562196 created "2017-01-06" @default.
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• W2562562196 date "2016-08-01" @default.
• W2562562196 modified "2023-09-26" @default.
• W2562562196 title "Median filtering the temporal probability distribution in histogram mapping for robust continuous speech recognition" @default.
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• W2562562196 doi "https://doi.org/10.1109/eusipco.2016.7760438" @default.
• W2562562196 hasPublicationYear "2016" @default.
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