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Kernel Methods forText-Independent Speaker Verification Chris…
mi.eng.cam.ac.uk/~mjfg/thesis_cl336.pdf25 Feb 2010: Kernel Methods forText-Independent. Speaker Verification. Chris Longworth. Cambridge University Engineering Departmentand. Christ’s CollegeFebruary 23, 2010. Dissertation submitted to the University of Cambridgefor the degree of Doctor of -
Department of Engineering 1 E�cient decodingwith continuous rational…
mi.eng.cam.ac.uk/~mjfg/Kernel/van_dalen-2012-tr-efficient_score-spaces.pdf27 Mar 2013: is requirement restricts the form of the kernel. e alternative,which this paper will use, is to work directly on the primal representation of the kernel.is means that the kernel ... φ(Osi,wi), (3). where Osi indicates the observations in segment si. In -
Int J Comput VisDOI 10.1007/s11263-012-0563-2 A Performance…
mi.eng.cam.ac.uk/~cipolla/publications/article/2012-IJCV-3D-interestpoints.pdf13 Mar 2018: The proposed combinedscore is computed based on repeatability ratio with respectto varying accuracy requirements. ... For each detector, the detected. Table 3 For each entry, top to bottom: The average number of interestpoints detected, the average -
Semantic Transform: Weakly Supervised Semantic Inference for Relating …
mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2013-ICCV-Shankar-attrirbutes.pdf13 Mar 2018: 3Note that the main requirement is that the classes are ordered so asto correctly reflect the underlying attribute-specific themes and the learntmodel maximally separates the classes while conforming to the ... all training images forattribute am, and -
Noname manuscript No.(will be inserted by the editor) Distances ...
mi.eng.cam.ac.uk/~cipolla/archive/Publications/article/2014-IJCV-Pham.pdf13 Mar 2018: Divergences, also sometimes referred to as qua-sisemimetrics, form a further superset of metrics whichalso drop the requirement of sub-additivity, requiringonly conditions 1 and 2. -
Discriminative Complexity Control and Linear Projections for Large…
mi.eng.cam.ac.uk/~mjfg/thesis_xl207.pdf16 Nov 2007: i=2 αi(τ )aij j = Ns, τ = T. (2.12). where Ns is the number of states in each HMM, including the non-emitting entry and exit states. ... moments to be stored as full matrices for each component. Again the computational requirement. -
newsletter.indd
mi.eng.cam.ac.uk/~cipolla/archive/Public-Understanding/2005-Insight-Tracking-Crowds.pdf7 Nov 2014: Hand in hand with this development, and working in close collaboration with the Cambridge Computational Biology Institute (CCBI), the aim is also to increase the breadth and capacity of our postgraduate ... There are clear benefi ts for both academic and -
This article appeared in a journal published by Elsevier. ...
mi.eng.cam.ac.uk/~cipolla/archive/Publications/article/2009-CVIU-face-illumination.pdf13 Mar 2018: training data requirements, promising resultsare reported by the more recent, generalized photometric stereomethods [13,14,16] which in addition exploit class-specific con-straints of face shape and albedo. ... Fig. 6. Our offline algorithm implicitly -
Linear Gaussian Models for Speech Recognition Antti-Veikko Ilmari…
mi.eng.cam.ac.uk/~mjfg/thesis_avir2.pdf16 Nov 2007: The diagram, adopted from. the hidden Markov model toolkit (HTK) [129], has non-emitting entry and exit states, and. ... The transitions from the non-emitting entry state are re-. estimated by â1j = γj(1) for 1 < j < Ns and the transitions from the -
Generation and Combination ofComplementary Systems for Automatic…
mi.eng.cam.ac.uk/~mjfg/thesis_cb404.pdf9 Jul 2008: varying signals [8], and has since formed the basis of many ASR systems.An HMM is a finite state machine, where the entry to each state has an associated outputdistribution, -
The State Based Mixture of Experts HMM with Applications ...
mi.eng.cam.ac.uk/reports/svr-ftp/tuerk_thesis.pdf2 Feb 2002: The State Based Mixture of Experts HMM. with Applications to the. Recognition of Spontaneous Speech. Andreas Tuerk. Emmanuel College. and. Cambridge University Engineering Department. September 2001. Dissertation submitted to the University of -
Lattice Rescoring Methods forStatistical Machine Translation Graeme…
mi.eng.cam.ac.uk/~wjb31/ppubs/gwbthesis2010.pdf6 Oct 2010: Lattice Rescoring Methods forStatistical Machine Translation. Graeme Blackwood. Cambridge University Engineering Departmentand. Clare College. Dissertation submitted to the University of Cambridgefor the degree of Doctor of Philosophy. -
Uncertainty Decoding forNoise Robust Speech Recognition Hank Liao…
mi.eng.cam.ac.uk/~mjfg/thesis_hl251.pdf17 Sep 2008: Uncertainty Decoding forNoise Robust Speech Recognition. Hank Liao. Sidney Sussex CollegeUniversity of Cambridge. September 2007. This dissertation is submitted for the degree ofDoctor of Philosophy to the University of Cambridge. Declaration. This -
Int J Comput VisDOI 10.1007/s11263-012-0563-2 A Performance…
mi.eng.cam.ac.uk/~cipolla/archive/Publications/article/2012-IJCV-3D-interestpoints.pdf13 Mar 2018: The proposed combinedscore is computed based on repeatability ratio with respectto varying accuracy requirements. ... For each detector, the detected. Table 3 For each entry, top to bottom: The average number of interestpoints detected, the average -
PROBABILISTIC ACOUSTIC MODELLING FOR PARAMETRIC SPEECH SYNTHESIS Sean …
mi.eng.cam.ac.uk/~wjb31/ppubs/shannon2014probabilistic-thesis.pdf2 Feb 2015: Here the parameter corresponding to y 7 yd is theentry bd of the b-value b, and the parameter corresponding to y 712ydye is the entry Pdeof the precision matrix P. -
Linear Gaussian Models for Speech Recognition
mi.eng.cam.ac.uk/reports/svr-ftp/rosti_thesis.pdf8 Oct 2004: The diagram, adopted from. the hidden Markov model toolkit (HTK) [130], has non-emitting entry and exit states, and. ... The transitions from the non-emitting entry state are re-. estimated by â1j = γj(1) for 1 < j < Ns and the transitions from the -
Department of Engineering 1 Generative Kernels and Score-Spaces…
mi.eng.cam.ac.uk/~mjfg/Kernel/rcv25_2013_y2.pdf9 Sep 2013: Requirements on the typesof weights that can be used in weights automata are well-established (Mohri 2009). ... Formany algorithms, including the forward algorithm, the requirement is that the weightsare in a semiring. -
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mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/niesler_tr281.pdf9 Aug 2005: The size of the tentative-list is of prime practical importance during first-pass processing, since each entry requires significantlymore storage than in the fixed-list. ... $% &(' () %%-,. / 0 %h11of the model has low memory requirements, the technique -
Named Entity Recognition from Speechand Its Use in the ...
mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/kim_thesis.pdf9 Aug 2005: of the requirement for the degree of Doctor of Philosophy. Abstract Page 1. ... requirements, absence of need for less-descriptive models as in back-off [54], and its easy ex-. -
Sparse and Semi-supervised Visual Mapping with the S3GP Oliver ...
mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2006-CVPR-Williams-sparse.pdf13 Mar 2018: the proportional decrease inthis requirement means that gaze tracking at 10–15Hz con-stitutes a “background task” leaving the majority of cyclesfree for other processes. ... If this paper is accepted forpresentation at CVPR, text entry with S3GP
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