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Highly spontaneous, conversational, and potentially emotional and noisy speech is known to be a challenge for today's automatic speech recognition (ASR) systems, which highlights the need for advanced algorithms that improve speech features and models. Histogram Equalization is an efficient method to reduce the mismatch between clean and noisy conditions by normalizing all moments of the probability distribution of the feature vector components. In this article, we propose to combine histogramdoi:10.1007/s11571-011-9166-9 pmid:22942915 pmcid:PMC3179540 fatcat:lfqo5jvmavgfdasr3my2j4xjvi