Samuel K. Ngouoko M

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We could show in the past that Hierarchical SpectroTemporal (HIST) features improve the performance of Automatic Recognition Systems (ARS) of speech in difficult environments when they are combined with conventional speech spectral features. The target here is to improve the noise robustness of the HIST features by investigating a channel distribution(More)
Previously, we applied a distribution equalization on our HIerarchical Spectro-Temporal (HIST) features using distributions estimated from histogram of one or several utterances. Although a performance increase could be observed in both cases, we noticed low performance improvement when estimating the distribution only from one utterance. The aim here is to(More)
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