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23 Oct 2023: 10 minutes reading time is allowed for this paper at the start ofthe exam.You may not start to read the questions printed on the subsequentpages of this question paper until

4 Nov 2018: The other three exam-ples illustrate situations where graphemic systems may struggle tomodel letter omission (’r’ in information), vowel (’oo’ in moon),consonant (’th’ in the) and vowel-consonant (’tio’ in ... A more general sys-tem

20 Feb 2018: treated as a class because there are not enough exam-ples of individual semantic trees in the data.

10 Nov 2015: Therefore the overall procedure is. 1. Initialise the parameters of the mixture, θ(0), - for exam-.

29 Sep 2023: Michaelmas term. 16 lectures (including 3 examples classes). Assessment: 100% exam.

20 Feb 2018: For exam-ple, the words ”arriving“ and ”Monday“ are neighbours in thedependency tree but they are four words apart in the utterance(see Figure 2).

13 Jun 2013: Future workwill exam the trend of these gains when deeper MLPs andcontext-dependent targets are used.

15 Jun 2018: The model is trained on ASR transcriptions of spokenresponses. Due to a lack of off-topic responses, negative exam-ples for both training and evaluation are created by randomlyshuffling prompts and ... is demon-strated on spoken data from the Cambridge

1 May 2024: More precisely,two-thirds of the way. Suitable past Tripos questions: Q16 on Paper 8 (section F) all exams 2010-2023.

20 Feb 2018: For exam-ple, [eat tagged-slot-value food] and [near tagged-slot-value] are clearly features related to food andarea slots respectively.

18 May 2015: What is more interesting is thatgraphemes not seen in the acoustic model training data, for exam-ple “һ”, exist in the recognition output (and correctly). ... Inaddition performance on a CTS Levantine Arabic task was exam-ined. In addition to the

20 Feb 2018: For exam-ple, the threshold for a phone p can be dened interms of the mean lp and variance rp of all theGOP scores for phone p in the training data,. ... For exam-ple, Fig. 2 shows the resulting network for theword but''.

15 Jun 2005: This is best illustrated by exam-ining maximum margin training of a univariate Gaussian class-conditional distribution.

20 Feb 2018: 5 Discriminative Training. In contrast to the traditional ML criteria (Equation 1)whose goal is to maximise the log-likelihood of cor-rect examples, DT aims at separating correct exam-ples

19 Jan 2012: This paper exam-ines one form of these models, structured support vector ma-chines (SVMs), for noise robust speech recognition.

20 Feb 2018: An l2 regularisation term is added tothe objective function for every 10 training exam-ples as suggested in Mikolov et al.

20 Feb 2018: The results also suggest that by matching word error rate, crowd sourcedphone-based testing can be a useful and economic surrogate for specific environ-ments such as the motor car.

10 Nov 2015: Web-Page: http://mi.eng.cam.ac.uk/mjfg/4F10/index.html. Assessment by exam (1.5h): 3 questions from 5. A number of books cover parts of the course material. •

16 Jun 2008: Only these 16 noise conditions are exam-ined in this work as stereo data is not provided for any of theother noise conditions.

22 Nov 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.