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Sparsity and Sentence Structure in Encoder-Decoder Attention of Summarization Systems ...
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Sparsity and Sentence Structure in Encoder-Decoder Attention of Summarization Systems ...
Manakul, Potsawee; Gales, Mark. - : Apollo - University of Cambridge Repository, 2021
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Sparsity and Sentence Structure in Encoder-Decoder Attention of Summarization Systems ...
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Long-Span Summarization via Local Attention and Content Selection ...
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Long-span summarization via local attention and content selection
Manakul, Potsawee; Gales, Mark. - : ACL-IJCNLP 2021 - 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Proceedings of the Conference, 2021
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6
Complementary Systems for Off-Topic Spoken Response Detection ...
Raina, Vatsal; Gales, Mark; Knill, Katherine. - : Apollo - University of Cambridge Repository, 2020
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7
Automatic detection of accent and lexical pronunciation errors in spontaneous non-native English speech ...
Kyriakopoulos, Konstantinos; Knill, Katherine; Gales, Mark. - : Apollo - University of Cambridge Repository, 2020
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8
Non-native children's automatic speech recognition: The INTERSPEECH 2020 shared task ALTA systems ...
Knill, Katherine; Wang, L; Wang, Y. - : Apollo - University of Cambridge Repository, 2020
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9
Non-native children's automatic speech recognition: The INTERSPEECH 2020 shared task ALTA systems
Knill, Katherine; Wang, L; Wang, Y. - : ISCA, 2020. : Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2020
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10
Complementary Systems for Off-Topic Spoken Response Detection
Raina, Vatsal; Gales, Mark; Knill, Katherine. - : Association for Computational Linguistics, 2020. : https://aclanthology.org/volumes/2020.bea-1/, 2020. : INNOVATIVE USE OF NLP FOR BUILDING EDUCATIONAL APPLICATIONS, 2020
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11
Impact of ASR performance on free speaking language assessment ...
Knill, Katherine; Gales, Mark; Kyriakopoulos, Konstantinos. - : Apollo - University of Cambridge Repository, 2018
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12
Phonetic and Graphemic Systems for Multi-Genre Broadcast Transcription ...
Wang, Yu; Chen, Xie; Gales, Mark. - : arXiv, 2018
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13
Phonetic and graphemic systems for multi-genre broadcast transcription ...
Wang, Yu; Chen, X; Gales, Mark. - : Apollo - University of Cambridge Repository, 2018
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14
Phonetic and graphemic systems for multi-genre broadcast transcription
Wang, Yu; Chen, X; Gales, Mark. - : IEEE, 2018. : https://ieeexplore.ieee.org/document/8462353, 2018. : ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2018
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15
Impact of ASR performance on free speaking language assessment
Knill, Katherine; Gales, Mark; Kyriakopoulos, Konstantinos. - : ISCA, 2018. : Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2018
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16
Student-teacher training with diverse decision tree ensembles ...
Wong, Jeremy; Gales, Mark. - : Apollo - University of Cambridge Repository, 2017
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17
Use of graphemic lexicons for spoken language assessment ...
Knill, Katherine; Gales, Mark; Kyriakopoulos, Konstantinos. - : Apollo - University of Cambridge Repository, 2017
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18
Use of graphemic lexicons for spoken language assessment
Knill, Katherine; Gales, Mark; Kyriakopoulos, Konstantinos; Ragni, Anton; Wang, Yu. - : ISCA, 2017. : Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2017
Abstract: Copyright © 2017 ISCA. Automatic systems for practice and exams are essential to support the growing worldwide demand for learning English as an additional language. Assessment of spontaneous spoken English is, however, currently limited in scope due to the difficulty of achieving sufficient automatic speech recognition (ASR) accuracy. "Off-the-shelf" English ASR systems cannot model the exceptionally wide variety of accents, pronunications and recording conditions found in non-native learner data. Limited training data for different first languages (L1s), across all proficiency levels, often with (at most) crowd-sourced transcriptions, limits the performance of ASR systems trained on non-native English learner speech. This paper investigates whether the effect of one source of error in the system, lexical modelling, can be mitigated by using graphemic lexicons in place of phonetic lexicons based on native speaker pronunications. Graphemicbased English ASR is typically worse than phonetic-based due to the irregularity of English spelling-to-pronunciation but here lower word error rates are consistently observed with the graphemic ASR. The effect of using graphemes on automatic assessment is assessed on different grader feature sets: audio and fluency derived features, including some phonetic level features; and phone/grapheme distance features which capture a measure of pronunciation ability.
URL: https://www.repository.cam.ac.uk/handle/1810/274280
https://doi.org/10.17863/CAM.21404
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19
Student-teacher training with diverse decision tree ensembles
Wong, Jeremy; Gales, Mark. - : ISCA, 2017. : Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2017
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20
Incorporating uncertainty into deep learning for spoken language assessment
Malinin, Andrey; Ragni, Anton; Knill, Katherine. - : Association for Computational Linguistics, 2017. : ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers), 2017
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