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Investigating the role of machine translated text in ASR domain adaptation: Unsupervised and semi-supervised methods.

, , , and . ASRU, page 260-265. IEEE, (2011)

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Logic-Based Rhetorical Structuring for Natural Language Generation in Human-Computer Dialogue., , and . TSD, volume 4629 of Lecture Notes in Computer Science, page 309-317. Springer, (2007)Automatic EMG-based Hand Gesture Recognition System using Time-Domain Descriptors and Fully-Connected Neural Networks., , , and . TSP, page 232-235. IEEE, (2019)Estimating competing speaker count for blind speech source separation., , , and . SpeD, page 1-8. IEEE, (2015)Text spotting in large speech databases for under-resourced languages., , and . SpeD, page 1-6. IEEE, (2013)Investigating the role of machine translated text in ASR domain adaptation: Unsupervised and semi-supervised methods., , , and . ASRU, page 260-265. IEEE, (2011)A constraint satisfaction approach to context-sensitive utterance generation in multi-party dialogue systems., , and . I. J. Speech Technology, 12 (2-3): 95 (2009)A Deep Learning Approach For Improved Segmentation Of Lesions Related To Covid-19 Chest CT Scans., , , , and . ISBI, page 635-639. IEEE, (2021)Detecting the number of competing speakers - human selective hearing versus spectrogram distance based estimator., , , and . INTERSPEECH, page 467-470. ISCA, (2014)Counting competing speakers in a timeframe - human versus computer., , , and . INTERSPEECH, page 3399-3403. ISCA, (2015)Design of Robust Complex-Valued Feed-Forward Neural Networks., , , and . EUSIPCO, page 1596-1600. IEEE, (2022)