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@conference{nmd2011,
author = "Antti Hurmalainen and Jort Gemmeke and Tuomas Virtanen",
abstract = "High noise robustness has been achieved in speech recognition by using sparse exemplar-based methods with spectrogram windows spanning up to 300 ms. A downside is that a large exemplar dictionary is required to cover sufficiently many spectral patterns and their temporal alignments within windows. We propose a recognition system based on a shift-invariant convolutive model, where exemplar activations at all the possible temporal positions jointly reconstruct an utterance. Recognition rates are evaluated using the AURORA-2 database, containing spoken digits with noise ranging from clean speech to -5 dB SNR. We obtain results superior to those, where the activations were found independently for each overlapping window.",
address = "Prague, Czech Republic",
booktitle = "Proceedings of International Conference on Audio, Speech and Signal Processing",
keywords = "non-negative matrix deconvolution;noise robustness;speech recognition",
month = "May",
organization = "IEEE Signal Processing Society",
title = "{N}on-negative matrix deconvolution in noise robust speech recognition",
url = "https://www.cs.tut.fi/sgn/arg/music/tuomasv/NMD_icassp2011.pdf",
year = "2011",
}