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PhD Thesis
mi.eng.cam.ac.uk/~mjfg/thesis_kcs23.pdf16 Nov 2007: 2.3 Limitations of HMMs for Speech Recognition 24. 2.3.1 Explicit Temporal Correlation Modelling 25. -
Discriminative Complexity Control and Linear Projections for Large…
mi.eng.cam.ac.uk/~mjfg/thesis_xl207.pdf16 Nov 2007: 2.5 HLDA and LDA projection 24. 2.6 multiple HLDA projections 25. -
Linear Gaussian Models for Speech Recognition Antti-Veikko Ilmari…
mi.eng.cam.ac.uk/~mjfg/thesis_avir2.pdf16 Nov 2007: 3.1 State Space Models 24. 3.2 Bayesian Networks 25. 3.3 State Evolution Process 27. ... bank of triangular filters (eg. 24 channels). This smoothed power spectrum is compressed using. -
A Gaussian Mixture Model Spectral Representation for Speech…
mi.eng.cam.ac.uk/~mjfg/thesis_mns25.pdf16 Nov 2007: 2.5.1 Vocal tract length normalisation 24. 2.5.2 Maximum likelihood linear regression 25. ... $ I / (2.23)3 /. $ $ I $ I - $ (2.24) $F 3 3 % / (2.25). -
PhD Thesis
mi.eng.cam.ac.uk/~mjfg/thesis_ky219.pdf16 Nov 2007: 2.4.1.2 Minimum Phone Error (MPE) 23. 2.4.1.3 Minimum Classification Error (MCE) 24. ... 2.4.2 Weak-Sense Auxiliary Function and Parameter Re-estimation 24. 2.5 Bayesian Training of HMMs 29. -
UNSUPERVISED TRAINING FOR MANDARIN BROADCAST NEWS AND…
mi.eng.cam.ac.uk/research/projects/AGILE/publications/wang_ICASSP07.pdf10 Oct 2007: SystemData bcmdev05. Select. ML MMI MPE. S0 — 29.2 26.7 25.3S1 — 26.1 23.3 21.8S2a — 27.7 25.9 24.7S2b CN08 27.9 25.8 ... 8S3a — 27.7 — 24.8S3b CN08 28.0 — 24.7. -
K. Yu, M.J.F. Gales and P.C. Woodland Cambridge University ...
mi.eng.cam.ac.uk/research/projects/AGILE/publications/yu-interspeech07.pdf10 Oct 2007: ML MPE ML MPE. S0 15.1 13.6 29.3 25.40.00 13.8 12.0 27.7 24.80.80 13.5 11.7 26.9 ... Conf. bnmdev06 bcmdev05Thresh. ML MPE ML MPE. 0.00 13.8 12.0 27.7 24.80.76 13.6 11.8 27.5 24.30.80 13.5 11.7 -
JOURNAL OF IEEE TRANS. ACOUST., SPEECH, SIGNAL PROCESSING, JULY ...
mi.eng.cam.ac.uk/research/projects/AGILE/publications/sim_SAP06.pdf10 Oct 2007: The set of parameters,Θ(sm),. 1Using this form of auxiliary function yields the same update formulae asusing the extended Baum-Welch (EBW) algorithm [24], [25]. ... Wmpem =B2D. 2m B1Dm B0β. (c)m Dm. (23). where. B2 = Σ̂m (24). -
IEEE TRANS. ON SAP, VOL. ?, NO. ??, ????? ...
mi.eng.cam.ac.uk/research/projects/AGILE/publications/liu_ASL07.pdf10 Oct 2007: This sensitivity to outliers is a well known feature of the MMI criterion [24]. ... j))}. (24). Each Gaussian component is assumed to be independent of all others. -
IEEE TRANS. ON SAP, VOL. ?, NO. ??, ????? ...
mi.eng.cam.ac.uk/research/projects/AGILE/publications/kai_ASP07.pdf10 Oct 2007: It is also interesting to compare N-Best supervisionto the standard 1-Best supervision adaptation approaches suchas iterative MLLR [24]. ... Zθ(O, H)can be simply calculated using the forward algorithm withp̃(ot|θt),. Zθ(O, H) =. θ. P (θ|M). t.
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