<P>PROBLEM TO BE SOLVED: To overcome the problem that a conventional general method is strongly affected by two major problems i.e. data sparsity and attribute interaction. <P>SOLUTION: A method includes a step of generating an initial time length predictive model, wherein each of a plurality of attributes and the attribute combination is included as an item by using part of a plurality of attributes concerning the time length prediction and possible attribute combination of the plurality of attributes; a step of calculating the importance of each item of the time length predictive model; a step of eliminating the item having the calculated minimum importance; a step of regenerating the time length predictive model with the rest of the items; a step of determining whether the regenerated time length predictive model is a suitable predictive model; and a step of repeating the importance calculating step and a step, following the importance calculating step with a newly regenerated time length predictive model, if it is determined that the step of calculating the importance and the time length predictive model are not the optimum model. <P>COPYRIGHT: (C)2007,JPO&INPIT 
METHOD AND DEVICE FOR TRAINING TIME LENGTH PREDICTIVE MODEL, METHOD AND DEVICE FOR TIME LENGTH PREDICTION, AND METHOD AND DEVICE FOR SPEECH SYNTHESIS