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Data Selection by Sequence Summarizing Neural Network in Mismatch Condition Training.

, , , , , , and . INTERSPEECH, page 2354-2358. ISCA, (2016)

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The language-independent bottleneck features., , , , and . SLT, page 336-341. IEEE, (2012)Multilingually trained bottleneck features in spoken language recognition., , , , , and . Comput. Speech Lang., (2017)BUT CHiME-7 system description., , , , , , , and . CoRR, (2023)New system theory and its impact on control theory.. Int. J. General Systems, 40 (2): 197-216 (2011)BUT BABEL system for spontaneous Cantonese., , , , and . INTERSPEECH, page 2589-2593. ISCA, (2013)DNN derived filters for processing of modulation spectrum of speech., , , and . INTERSPEECH, page 1908-1911. ISCA, (2015)Semi-Supervised DNN Training with Word Selection for ASR., , and . INTERSPEECH, page 3687-3691. ISCA, (2017)"Semi-supervised" trénování hlubokých neuronových sítí pro rozpoznávání řeči ; Semi-Supervised Training of Deep Neural Networks for Speech Recognition.. Brno University of Technology, Czech Republic, (2018)base-search.net (ftunivtbrno:oai:dspace.vutbr.cz:11012/187318).Convolutive Bottleneck Network features for LVCSR., , and . ASRU, page 42-47. IEEE, (2011)Manual and semi-automatic approaches to building a multilingual phoneme set., , , , and . ICASSP, page 7324-7328. IEEE, (2013)