P09-1014 discriminative ranking approach to lexical stress prediction , which clearly outperforms previously
D13-1088 ranking-based approach to Russian stress prediction . The approach is similar in
P09-1014 evaluate various methods of combining stress prediction with phoneme gener - ation .
E14-4013 important and helps the task of stress prediction . The cascaded sequential model
P09-1014 5.1 The L2P system We combine stress prediction with a state-of-theart L2P system
E14-4013 CRFsuite ( Okazaki , 2007 ) . For the stress prediction model we optimize hyperparameters
A83-1032 achieve this improvement , a set of stress prediction rules amenable to computer implementation
P09-1014 long history of computational stress prediction systems ( Fudge , 1984 ; Church
D13-1088 et al. ( 2009 ) , we frame the stress prediction problem as a ranking problem
P09-1014 then discuss the different ways stress prediction can be integrated with L2P ,
E14-4013 Approach We address the task of stress prediction for Romanian words ( out-of-context
A83-1032 ) . It applies the theory for stress prediction proposed by linguists Mark Liberman
D13-1088 for the primary plus secondary stress predictions , were highly significant in
D13-1088 Abstract We explore a model of stress prediction in Russian using a combination
E14-4013 is interesting to notice that stress prediction accuracy is almost equal for
P09-1014 vs. be-at-i-fy ) . 3 Automatic Stress Prediction Our stress assignment system
P09-1014 evaluate our ranking approach to stress prediction by assigning stress to spoken
D13-1088 further . For primary and secondary stress prediction ( column 3 in the table ) , the
P09-1014 <title> A Ranking Approach to Stress Prediction for Letter-to-Phoneme Conversion
D13-1088 homophonic cipher . <title> Russian Stress Prediction using Maximum Entropy Ranking
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