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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 ...
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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
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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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Complementary Systems for Off-Topic Spoken Response Detection ...
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Automatic detection of accent and lexical pronunciation errors in spontaneous non-native English speech ...
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Non-native children's automatic speech recognition: The INTERSPEECH 2020 shared task ALTA systems ...
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Non-native children's automatic speech recognition: The INTERSPEECH 2020 shared task ALTA systems
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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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Complementary Systems for Off-Topic Spoken Response Detection
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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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Impact of ASR performance on free speaking language assessment ...
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Phonetic and Graphemic Systems for Multi-Genre Broadcast Transcription ...
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Abstract:
State-of-the-art English automatic speech recognition systems typically use phonetic rather than graphemic lexicons. Graphemic systems are known to perform less well for English as the mapping from the written form to the spoken form is complicated. However, in recent years the representational power of deep-learning based acoustic models has improved, raising interest in graphemic acoustic models for English, due to the simplicity of generating the lexicon. In this paper, phonetic and graphemic models are compared for an English Multi-Genre Broadcast transcription task. A range of acoustic models based on lattice-free MMI training are constructed using phonetic and graphemic lexicons. For this task, it is found that having a long-span temporal history reduces the difference in performance between the two forms of models. In addition, system combination is examined, using parameter smoothing and hypothesis combination. As the combination approaches become more complicated the difference between the phonetic ... : 5 pages, 6 tables, to appear in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018) ...
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Keyword:
Audio and Speech Processing eess.AS; Computation and Language cs.CL; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Sound cs.SD
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URL: https://dx.doi.org/10.48550/arxiv.1802.00254 https://arxiv.org/abs/1802.00254
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Phonetic and graphemic systems for multi-genre broadcast transcription ...
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Phonetic and graphemic systems for multi-genre broadcast transcription
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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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Impact of ASR performance on free speaking language assessment
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Student-teacher training with diverse decision tree ensembles ...
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Use of graphemic lexicons for spoken language assessment ...
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Student-teacher training with diverse decision tree ensembles
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Wong, Jeremy; Gales, Mark. - : ISCA, 2017. : Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2017
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Incorporating uncertainty into deep learning for spoken language assessment
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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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