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5 Oct 2000: 4 IMPROVING MMIE GENERALISATION. An important issue in MMIE training is the ability to generalise tounseen test data. ... The results of these experiments on both theeval97sub and eval98 test sets are shown in Table 4.

8 Mar 2000: In tests the fastercache led to 6-8% faster decoder operation on a Xeon than onthe equivalent Pentium II. ... Further tests under different operating systems indicated that. compiler efficiency was also an important issue.

8 Mar 2000: Two versions of the HTK system were used in the experimentson TREC-6 SDR test data. ... This system gave a word error rate of 24.1% on the TREC-6 test data and ran in about 50 times real time.

8 Mar 2000: approach, and two sets of HMMs were adapted.The test data for F2 was also automatically labelled as narrow. ... 1995 Hub-4 “Dry-Run” Broadcast Materials Bench-mark Tests. Proc. DARPA Speech Recognition Workshop, Har-riman, New York.

8 Mar 2000: Figure 3: Network prediction on training and test data. Actual output[.

5 Oct 2000: While MMIE is very effective at reducing training set error a keyissue is generalisation to test data. ... On other test setsimprovements greater than 1% absolute have also been obtained and.

8 Mar 2000: 9 26 142. Table 1: Training and test material available in broadcast news (min-utes) for music (M) background speaker (BGS), other background(BGO), music and speech (MS), narrow-band (T) ... The test set is split into shows available bothin training and

8 Mar 2000: news development test data andjust 15.8% on the 1997 evaluation test set. ... The OOV rate on both these test sets using the filteredtranscriptions was about 0.5%.

6 Nov 2000: ing in general) is the generalisation performance i.e. the dif-ference between training set and test set accuracy. ... Baseline Models and Hub5 Training/Test DataThree different training sets and three different test sets.

31 Jul 2000: nemea. Training data Average log2-prob (eciency). ICSI-test TIMIT-test. TIMIT 0.76! 0.60. ... derivation of the training transcrip-tions, and the hand-labelling of the test set.

31 Jul 2000: Pronunciation modeling by sharing Gaussians 141. TABLE I. WER degradation with speaking style on theMULTI-REG test set. ... Steps (6), (3), (4) and (5) arethen carried out in that order to estimate and test a matched pronunciation model.

10 Apr 2000: spontaneous non-native). Table 1: Broadcast news focus conditions. For development test purposes, data broadcast in July 1996from six shows was used. ... Overall 19.0 21.0. Table 9: % Word error rates on the Marketplace dev-test show forautomatic and

2 Nov 2000: 4 ExperimentsThe full HUB-4 1997 evaluation test set was used to as-sess language model performance.1 Although the trainingtext described in section 3 was chosen to try and matchthe ... Treating this asthe baseline result to improve upon, statistical

8 Mar 2000: During system development theBNeval97 test set was used. 4.1. Increased Acoustic Training Data. ... All these sources ex-cluded data from the designated test epochs. This corpus wasdenoted LMtrain98.

5 Oct 2000: It canbe seen that for the eval00 test set the unmapped posteriors performmuch better than for the eval98 set. ... Table 4: System Combination Results. The improvement over the single best system achieved byROVER are rather disappointing especially on

8 Mar 2000: Development work on the TREC-6 test corpus was done using twoHTK based systems. ... But more importantly, the test data is too small for reliable andinformative inference.

10 Apr 2000: Preprocess & apply SPI to the test collection to giveIt2. Preprocess & apply SPI to parallel collection to giveIp3. ... This contains the TREC-8 SDR test collectionperiod (Feb 1st to June 30th 1998).

25 Oct 2000: The test sequence(seefigure 8 ) consistsof a hollow unit cubewith textured imageson the back threefaces.This configurationwaschosenbecauseit hasa largehollow volumethathasto becarvedaway, and the exterior boundarygives no information aboutthe

10 Apr 2000: 1. INTRODUCTION. For speech transcription problems with widely varyingspeaker, channel and background conditions, such asbroadcast news transcription, it is beneficial to include un-supervised test-data adaptation to individual speakers anddata

10 Apr 2000: The output of the first pass is used alongwith a top-down covariance-based segment clusteringalgorithm [10] to group segments within each show toperform unsupervised test-set adaptation using maxi-mum ... 5. RESULTS. The five systems described in section4