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TPAs are economic, off-the-shelf, and they have several interesting properties. They can form flexible or rigid structures by means of existing fast-prototyping techniques and can be also used

17 Nov 2017: Motivation:. › Computer assisted language learning (CALL). › Auto-marking of oral exams. ›

9 Dec 2006: For exam-ple, speaker models can be built for people who are likely to be inthe broadcast (such as prominent politicians or main news anchorsand reporters). ... However, this method relies on previously seen exam-ples of the test speakers, which is not

17 Nov 2017: Budapest University of Technology and Economics, Department of Telecommunication and Media Informatics,.

9 Dec 2006: For exam-ple, speaker models can be built for people who are likely to be inthe broadcast (such as prominent politicians or main news anchorsand reporters). ... However, this method relies on previously seen exam-ples of the test speakers, which is not

2 Nov 2000: 4.2 PerplexityAlthough the experiments reported here are concerned withimproving recognition accuracy, it is interesting to exam-ine perplexity figures too.

5 May 2000: We have also exam-ined the application of word posteriors to the estimation ofconfidence scores.

8 Aug 2005: exam-ple,by AthitsosandSclaroff for handposeestimation[1] andShakhnarovich et al.

22 May 2004: One exam-ple is the stochastic segment model (SSM). This uses a standardlinear dynamical system (LDS) to model the sequence of observa-tions within a segment.

26 Sep 2005: SPLICE [1] is one recent exam-ple of this approach. Alternatively in model-based approaches,the parameters of the system are altered to reflect speech in thenew acoustic environment.

8 Aug 2005: Training examplesEach training image can generate multiple training exam-ples. Each example is a vector of feature responses takenat a particular centroid and is given a binary target value,positive, meaning ... After each round, all training exam-ples

25 Mar 2005: This reduces the num-ber of all possibleclusterings of the base-segments for this exam-ple from Bell(9) = 21, 147 to 2 Bell(3) = 10.

9 Aug 2005: Table 1 showhow different tracking modes can be effected. For exam-ple if a rigid curve is to be tracked under variable epipolargeometry (eg.

5 Dec 2003: For exam-ple each window has been made reflective and partiallytransparent, so that it exhibits specular reflection as theviewer moves past it, as can be seen in Figure 5(h)-(i).

23 Feb 2006: However discriminative training1 has also been exam-ined within this framework [3, 4].

14 Sep 2006: The results we show later, and the exam-ples in figure 3 confirm that there is indeed a practical advantage, but it is instructive toconsider on an intuitive level what the

9 Aug 2005: For efficiency, the geometric feature vector & is exam-ined first. If the feature vector fails to be a valid instance of.

23 Feb 2006: For exam-ple, the combination of the S2 and S3 GAUSS systems gave a 0.3%and 0.2% absolute improvements oneval03m and eval04 re-spectively.

7 Oct 2009: 4. NETWORK ADAPTATION. The standard approach for speaker adaptation when using MLP fea-tures is simply to use the same form of adaptation applied, for exam-ple, to PLP-based systems.

19 Mar 2006: W1 was found to be very large). Exam-ples are shown in Fig.