Information-theoret for Unit Selection S

In our recent work on concatenative speech synthesis, we have devised an efficient, graph-based search to perform unit selection given symbolic information. By encapsulating concatenation and substitution costs defined at the class level, the graph expands only linearly with respect to corpus size. To date, these costs were manually tuned over pre-specified classes, which was a knowledgeintensive engineering process. In this research paper, we turn to information-theoretic metrics for automatically learning the costs from data. These costs can be analyzed in a minimum description length (MDL) framework. The performance of these automatically determined weights is compared against that of manually tuned weights in a perceptual evaluation.