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• W2016076298 abstract "In this work, we propose a deep bottleneck feature architecture that is able to leverage data from multiple languages. We also show that tonal features are helpful for non-tonal languages. Evaluations are performed on a low-resource conversational telephone speech transcription task in Bengali, while additional data for DBNF training is provided in Assamese, Pashto, Tagalog, Turkish, and Vietnamese. We obtain relative reductions of up to 17.3% and 9.4% WER over mono-lingual GMMs and DBNFs, respectively." @default.
• W2016076298 created "2016-06-24" @default.
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• W2016076298 date "2014-05-01" @default.
• W2016076298 modified "2023-09-25" @default.
• W2016076298 title "Multilingual shifting deep bottleneck features for low-resource ASR" @default.
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• W2016076298 doi "https://doi.org/10.1109/icassp.2014.6854676" @default.
• W2016076298 hasPublicationYear "2014" @default.
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