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21 - 28 of 28 search results for KaKaoTalk:ZA31 24 24 |u:mi.eng.cam.ac.uk where 0 match all words and 28 match some words.

  1. Results that match 2 of 3 words

  2. JOURNAL OF IEEE TRANS. ACOUST., SPEECH, SIGNAL PROCESSING, JULY ...

    mi.eng.cam.ac.uk/research/projects/AGILE/publications/sim_SAP06.pdf
    10 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).

  3. PhD Thesis

    mi.eng.cam.ac.uk/~mjfg/thesis_kcs23.pdf
    16 Nov 2007: 2.3 Limitations of HMMs for Speech Recognition 24. 2.3.1 Explicit Temporal Correlation Modelling 25.

  4. PhD Thesis

    mi.eng.cam.ac.uk/~mjfg/thesis_ky219.pdf
    16 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.

  5. IEEE TRANS. ON SAP, VOL. ?, NO. ??, ????? ...

    mi.eng.cam.ac.uk/research/projects/AGILE/publications/kai_ASP07.pdf
    10 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.

  6. Discriminative Complexity Control and Linear Projections for Large…

    mi.eng.cam.ac.uk/~mjfg/thesis_xl207.pdf
    16 Nov 2007: 2.5 HLDA and LDA projection 24. 2.6 multiple HLDA projections 25.

  7. Linear Gaussian Models for Speech Recognition Antti-Veikko Ilmari…

    mi.eng.cam.ac.uk/~mjfg/thesis_avir2.pdf
    16 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.

  8. A Gaussian Mixture Model Spectral Representation for Speech…

    mi.eng.cam.ac.uk/~mjfg/thesis_mns25.pdf
    16 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).

  9. ATK An Application Toolkit for HTK Version 1.6 Steve ...

    mi.eng.cam.ac.uk/research/dialogue/ATK_Manual.pdf
    20 May 2007: TARGETKIND = MFCC_E_D_N_A. TARGETRATE = 100000.0. USEPOWER = F. NUMCHANS = 24. ... 24. void ATask::FooCmd(). {. int i;. if (GetIntArg(i,0,100)) {. // implement foo(i). }. }.

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