Lsm-Based Boundary Training for Concatenative Speech Synthesis

The level of quality that can be achieved in concatenative text-to-speech synthesis depends, among other things, on a judicious chiseling of the inventory used in unit selection. Unit boundary optimization, in particular, can make a huge difference in the users´ perception of the concatenated acoustic waveform. This paper considers the iterative refinement of unit boundaries based on a data-driven feature extraction framework separately optimized for each boundary region. Such unsupervised boundary training guarantees a globally optimal cut point between any two matching units in the inventory. This optimization is objectively characterized, first in terms of convergence behavior, and then by comparing the average inter-unit discontinuity obtained before and after training. Experimental results and listening evidence both underscore the viability of this approach for unit boundary optimization