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Latent Intention Dialogue Models Tsung-Hsien Wen 1 * Yishu ...
mi.eng.cam.ac.uk/~sjy/papers/wmby17.pdf20 Feb 2018: goodbye. ( 1 0.24) you are welcome. goodbye. ( 85 0.19) is there anything else i can help you with? -
williams2005continuous06
mi.eng.cam.ac.uk/~sjy/papers/wipy05c.pdf20 Feb 2018: 1)(|( erruincorrecterrucorrect pacppacp = (24). In the MDP context, we assume the confidence score buckets are formed without access to a prior. )( -
Kate Knill ALTA Institute, Cambridge University Engineering…
mi.eng.cam.ac.uk/~kmk/presentations/Knill_UKSpeech2023_Keynote.pdf19 Jun 2023: conversational assessment. 24. Applying Foundation Models to Auto-marking: Neural Text Grader. -
Machine Intelligence Laboratory
mi.eng.cam.ac.uk/Main/GMT_EqnSurfIn the Windows version this will be saved in uncompressed 24-bit. -
Roberto Cipolla - Professor of Information Engineering
mi.eng.cam.ac.uk/~cipolla/professional.htmJuly 23-24, 1997. Organiser and Co-Chair of the European Conference on Computer Vision 1996,. -
Roberto Cipolla - Professor of Information Engineering
mi.eng.cam.ac.uk/~cipolla/publications.htmImage and Vision Computing, 24(6):639-647, June 2006. O. Arandjelovic and R. -
Abstract for hain_icslp00
mi.eng.cam.ac.uk/reports/abstracts/hain_icslp00.html27 Jul 2020: Experiments on the Resource Management (RM) corpus and a subset of the Switchboard corpus show that, relative to standard HMM baseline, a reduction word error rate (WER) of 24.3% relative -
Abstract for johnson_trec7
mi.eng.cam.ac.uk/reports/abstracts/johnson_trec7.html27 Jul 2020: The broadcast news audio was transcribed using a 2-pass gender-dependent HTK speech recogniser which ran at 50 times real time and gave an overall word error rate of -
Royal Society Meeting on Geometry in Computer Vision
mi.eng.cam.ac.uk/~cipolla/royal_society.html28 Nov 2006: Session 3: Grouping and Matching. Thursday 24 July, 09.30-12.30 (Chair: Dr A. ... Session 4: Geometry and Statistics. Thursday 24 July, 14.00-17.40 (Chair: Dr R. -
Abstract for clarkson_icassp97
mi.eng.cam.ac.uk/reports/abstracts/clarkson_icassp97.html27 Jul 2020: Both techniques yield a significant reduction in perplexity over the baseline trigram language model when faced with multi-domain test text, the mixture-based model giving a 24% reduction and the
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