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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 – 6 of 6 1 S-NER: A Concise and Efficient Span-Based Model for Named Entity Recognition Jie Yu; Bin Ji; Shasha Li; Jun Ma; Huijun Liu; Hao Xu In: Sensors; Volume 22; Issue 8; Pages: 2852 (2022) BASE Show details 2 Ontology Guided Sparse Tensor Factorization for joint recommendation with hierarchical relationships [<Journal>] Liu, Hao [Verfasser]; Shi, Xiutao [Verfasser]; Li, Guangxi [Verfasser]. DNB Subject Category Language Show details 3 UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Gao, Can; Li, Wei. - : Underline Science Inc., 2021 BASE Show details 4 Functional maintenance in the multiple demand network characterizes superior fluid intelligence in aging Zuo, Nianming; Salami, Alireza; Liu, Hao. - : Elsevier, 2020 BASE Show details 5 Measurement of $W^{\pm}$-boson and $Z$-boson production cross-sections in $pp$ collisions at $\sqrt{s}=2.76$ TeV with the ATLAS detector Aad, Georges; Abbott, Brad; Abbott, Dale Charles. - 2019 BASE Show details 6 Dynamic Bayesian Network-based Speech Synthesis Liu, Hao. - : The University of Edinburgh, 2012 Abstract: As the simplest version of dynamic Bayesian network (DBN), hidden Markov model (HMM) has its natural limits in speech synthesis in terms of explicit segmental and suprasegmental prosodic properties modelling (e.g. phone duration, syllable duration, F0 contour at syllable level, etc.). In stead of continuing to explore new “add-ons” for the existing HMM-based speech synthesis system, this dissertation makes a new attempt by doing speech synthesis under the complete DBN framework. The Graphical Models toolkit (GMTK) is used to implement such a novel system. As described in the dissertation, the DBN-based speech synthesis prototype system is a self-contained one, i.e. all the features are modelled within a standard DBN. The dissertation first introduces the development and current issues of speech synthesis, then explains HMM and its relation with (dynamic) Bayesian networks. After the theory part, the dissertation describes and illustrates in detail how the DBN-based system is built and how phone duration is explicitly modelled step by step. Finally, a brief object evaluation is conducted. Since the current DBN-based system is just a prototype, many problems are existed. However, such a DBN-based system does direct us a bright future. Keyword: DBN; Dynamic Bayesian Network; GMTK; HMM; Speech Synthesis URL: http://hdl.handle.net/1842/8508 BASE Hide details

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