LSM-based unit pruning for concatenative speech synthesis

The level of quality that can be achieved in concatenative text-to-speech synthesis is primarily governed by the inventory of units used in unit selection. This has led to the collection of ever larger corpora in the quest for ever more natural synthetic speech. As operational considerations limit the size of the unit inventory, however, pruning is critical to removing any instances that prove either spurious or superfluous. This paper proposes a novel pruning strategy based on a data-driven feature extraction framework separately optimized for each unit type in the inventory. A single distinctiveness/redundancy measure can then address, in a consistent manner, the (traditionally separate) problems of outliers and redundant units. Experimental results underscore the viability of this approach for both moderate and aggressive inventory pruning.