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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 1 of 1 1 Development of a TV Broadcasts Speech Recognition Mohamed Elmahdy; Mark Hasegawa-johnsony; Eiman Mustafawi In: http://www.isle.illinois.edu/sst/pubs/2014/elmahdy_qa_lrec14.pdf Abstract: A major problem with dialectal Arabic speech recognition is due to the sparsity of speech resources. In this paper, a transfer learning framework is proposed to jointly use a large amount of Modern Standard Arabic (MSA) data and little amount of dialectal Arabic data to improve acoustic and language modeling. The Qatari Arabic (QA) dialect has been chosen as a typical example for an under-resourced Arabic dialect. A wide-band speech corpus has been collected and transcribed from several Qatari TV series and talk-show programs. A large vocabulary speech recognition baseline system was built using the QA corpus. The proposed MSA-based transfer learning technique was performed by applying orthographic normalization, phone mapping, data pooling, acoustic model adaptation, and system combination. The proposed approach can achieve more than 28 % relative reduction in WER. Keyword: dialectal Arabic; speech recognition; transfer learning URL: http://www.isle.illinois.edu/sst/pubs/2014/elmahdy_qa_lrec14.pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.641.905 BASE Hide details

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