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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 – 7 of 7 1 Evaluating College English Textbooks for Chinese Students’ English Academic Writing: Voices of Students and Teachers’ Li, J; Cui, X. - 2021 BASE Show details 2 Multilingual representations for low resource speech recognition and keyword search Cui, J.; Kingsbury, B.; Ramabhadran, B.; Sethy, A.; Audhkhasi, K.; Cui, X.; Kislal, E.; Mangu, L.; Nussbaum-Thom, M.; Picheny, M.; Tüske, Z.; Golik, P.; Schlüter, R.; Ney, H.; Gales, M.J.F.; Knill, K.M.; Ragni, A.; Wang, H.; Woodland, P.. - : IEEE, 2016 Abstract: This paper examines the impact of multilingual (ML) acoustic representations on Automatic Speech Recognition (ASR) and keyword search (KWS) for low resource languages in the context of the OpenKWS15 evaluation of the IARPA Babel program. The task is to develop Swahili ASR and KWS systems within two weeks using as little as 3 hours of transcribed data. Multilingual acoustic representations proved to be crucial for building these systems under strict time constraints. The paper discusses several key insights on how these representations are derived and used. First, we present a data sampling strategy that can speed up the training of multilingual representations without appreciable loss in ASR performance. Second, we show that fusion of diverse multilingual representations developed at different LORELEI sites yields substantial ASR and KWS gains. Speaker adaptation and data augmentation of these representations improves both ASR and KWS performance (up to 8.7% relative). Third, incorporating un-transcribed data through semi-supervised learning, improves WER and KWS performance. Finally, we show that these multilingual representations significantly improve ASR and KWS performance (relative 9% for WER and 5% for MTWV) even when forty hours of transcribed audio in the target language is available. Multilingual representations significantly contributed to the LORELEI KWS systems winning the OpenKWS15 evaluation. URL: http://eprints.whiterose.ac.uk/152836/ https://ieeexplore.ieee.org/document/7404803 BASE Hide details 3 Multilingual representations for low resource speech recognition and keyword search ... Cui, J; Kingsbury, B; Ramabhadran, B. - : Apollo - University of Cambridge Repository, 2015 BASE Show details 4 Multilingual representations for low resource speech recognition and keyword search Cui, J; Kingsbury, B; Ramabhadran, B. - : IEEE, 2015. : 2015 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2015 - Proceedings, 2015 BASE Show details 5 Acoustic Modeling for Automatic Speech Recognition - Multi-View and Multi-Objective Semi-Supervised Learning for HMM-Based Automatic Speech Recognition Cui, X; Huang, J; Chien, J-T In: Institute of Electrical and Electronics Engineers. IEEE transactions on audio, speech and language processing. - New York, NY : Inst. 20 (2012) 7, 1923-1935 OLC Linguistik Show details 6 Acoustic Modeling for Automatic Speech Recognition - Hidden Markov Acoustic Modeling With Bootstrap and Restructuring for Low-Resourced Languages Cui, X; Xue, J; Chen, X... In: Institute of Electrical and Electronics Engineers. IEEE transactions on audio, speech and language processing. - New York, NY : Inst. 20 (2012) 8, 2252-2264 OLC Linguistik Show details 7 Speech Recognition - Robust Speaker Adaptation by Weighted Model Averaging Based on the Minimum Description Length Criterion Cui, X; Alwan, A In: Institute of Electrical and Electronics Engineers. IEEE transactions on audio, speech and language processing. - New York, NY : Inst. 15 (2007) 2, 652 OLC Linguistik Show details

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