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17 Nov 2017: 3. Experimental Results: Automatic Speech Recognition. Data from Business Language Tests (BULATS)I Up to 1 minute spontaneous responses to promptsI ASR training data: 100 hours Gujarati L1 English speechI Test ... Data from Business Language Tests (BULATS

19 Apr 2017: graders for high-stakes tests, maximizing theincrease in performance while rejecting the leastnumber of candidates. ... Lucy Chambers and Kate Ingham. 2011. The BULATSonline speaking test. Research Notes 43:21–25.

23 Dec 2017: cipan. t rat. ing. Figure 6: Ranksum test. Median rating & 95% confidence interval. ... Stimulation is added to improve interpretability• Evaluation is performed with a perceptual test.

22 Mar 2017: If thenormalisation termZ(hi) could be approximated as constantC, un-normalised RNNLM probabilities are be used in test time as,. ... In test time, RNNLM probabilities canbe approximated as unnormalised probabilities in Eqn (3) and fastevaluation speed

21 Nov 2017: descent using Theano.Evaluation: (i) on all frames in the core test set, (ii) on only centreframes of phone segments (also need to finetune DNN). ... Experimental Results - Phone Classification Performance. (“” indicates a pass of McNamar’s

20 Nov 2017: MI_train: 8h segmented and transcribed data • MI_test: 6 interviews,0.7h in total. • ... Table 1. The ASR results on MI_test. Fmllr feature vectors. Baseline DNN MFCC feature vectors.

20 Nov 2017: A development set, ST-DEV, of 5222recordings and a test set, ST-TST, of 996 recordings were released.System structure:. ... 1 1 0 1 0.  log(score(x) )Step2: Use linear logistic regression to train weights on K systems.Step3: Apply weights on test

23 Dec 2017: Our SI tests use 12 maps derivedusing all speakers confusions bar the test speaker. ... in these tests wetrained and test on the same speaker (maintaining independent samples between train andtest folds).

17 Nov 2017: Figure 2: Prototype avatar. Results. A.Speech recognition. Table 1: Speech recognition results.System Train Test WER. ... Train/Test CA AC LX ALL T10A 96.7% 83.3% 66.7% 76.7% 100%B 76.7% 60.0% 50.0% 76.7% 90.0%C 58.3%

17 Nov 2017: Bottleneck Layer. HLDA. DNN. HLDA. Fusion. Score. LSTM. FBank Test set%WER.

17 Nov 2017: I Non-parametric Bayesian model: fGP(x;D) µg (x),σ2g (x)I Uncertainty depends on proximity of test data to training dataI Limitations - O(n2) memory, O(n3) compute use

22 Mar 2017: If thenormalisation termZ(hi) could be approximated as constantC, un-normalised RNNLM probabilities are be used in test time as,. ... In test time, RNNLM probabilities canbe approximated as unnormalised probabilities in Eqn (3) and fastevaluation speed

20 Nov 2017: sampled from 12 shows).I Test set : dev.sub2 audio from the remaining 35 shows in the.

9 May 2017: using 793K vocabulary. The train is run on GPU and test is on CPU. ... 126. 8.6 Out of Vocabulary (OOV) % for AMI and TTM test data.

18 May 2017: 1. Thou shalt not train on test data. 2. Thou shalt not determine hyperparameters on test data. ... set it aside for use as test data. If you train on this data,.

23 Dec 2017: sentences 1 to n for speaker N to train, andsentences n 1 to 1,000 for test. ... sentences 1 to. n for speaker X to train, and sentences n1 to 1000 for test.

14 Jan 2017: Network Multiply-Acc. 109 Test M.A. w/ Aug. 109 Param. 107 Top-5 Acc. ... Test time parameters v.s.top-5 error for state of the art models.

18 May 2017: approximately 250 individuals. There are 60,000 training im-. ages and 10,000 test images (sampled in a disjoint manner.

14 Jan 2017: Network Multiply-Acc. 109 Test M.A. w/ Aug. 109 Param. 107 Top-5 Acc. ... Test time parameters v.s.top-5 error for state of the art models.

20 Nov 2017: I Full test set: 1344 utterances from 168 speakers, 0.81 hours.I 13 dimensional MFCC features with and.