W15-5112 , speaker adaptation , speaker adaptive training 1 . Introduction Dysarthria is
W15-4617 models are obtained with speaker adaptive training ( SAT ) on the feature Maximum
N10-1062 chal - lenge . We introduce an adaptive training regime using an online variant
N04-4007 normalized and estimated using Speaker Adaptive Training ( SAT ) . The SAT models are
W11-0125 den Bosch ( 1997 ) is to apply adaptive training using the predicted output of
W15-5112 . <title> Model adaptation and adaptive training for the recognition of dysarthric
W15-1825 speaker-specific data . Speaker Adaptive Training ( SAT ) techniques such as Constrained
P10-1036 accurate as the baseline system . Adaptive training is also an effective method of
W15-5112 explores the usefulness of speaker adaptive training ( SAT ) for implicitly anni -
N09-2049 with three iterations of speaker adaptive training ( SAT ) using constrained maximum
H90-1103 the patterns are trained , and adaptive training techniques \ -LSB- 99 \ -RSB-
W98-1223 refer to this type of training as adaptive training , referring to the adaptation
W15-5113 vector ( SSV ) of phone-cluster adaptive training ( Phone-CAT ) acoustic model
H90-1087 itself . FY-91 Plans * On-line adaptive training will be implemented . * The signal
N09-2049 erroneous regions shows how speaker adaptive training ( SAT ) and discriminative training
H90-1103 desirable would be a system with adaptive training , which learns to extend its
P10-1036 Marneffe et al. , 2006 ) . For adaptive training we have used 1,900,618,859 tokens
P13-1005 modeling and CMLLR-based speaker adaptive training and 4 iterations of boosted MMI
W15-5113 State-Specific Vectors of Phone-Cluster Adaptive Training </title> R S M Ramasubba Reddy
W15-5112 investigate the efficacy of speaker adaptive training ( SAT ) -LSB- 22 -RSB- to implicitly
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