Unit Selection Algorithm Research Articles

Modeling stylized invariance and local variability of prosody in text-to-speech synthesis

This paper investigates the stylized invariance and local variability of prosody patterns by using a speech database containing two repetitions of 1000 sentences. The two repetitions (separated by a time span of 6 months) were recorded by a single professional speaker, who was instructed to read these sentences in the same reading style. It was observed statistically that the two repetitions have fairly wide variations in prosodic features and the variations can be up to 50% of the full dynamic range of the speaker. This shows the inadequacy of traditional prosody models that focus on capturing the universal invariance of prosody as precise as possible. In this paper, we propose to model prosody by capturing its stylized invariance and retaining local variability with a soft prediction strategy, which predicts an acceptable region rather than a single fixed point in the multi-dimensioned prosody space. A prosodic-constrained unit selection algorithm is devised under the soft prediction strategy.

A Text-to-Speech Platform for Variable Length Optimal Unit Searching Using Perception Based Cost Functions

In concatenative Text-to-Speech, the size of the speech corpus is closely related to synthetic speech quality. In this paper, we describe our work on a new corpus-based Bell Labs' TTS system. This encompasses large acoustic inventories with a rich set of annotations, models and data structures for representing and managing such inventories, and an optimal unit selection algorithm that accommodates a broad range of possible cost criteria. We also propose a new method for setting weights in the cost functions based on a perceptual preference test. Our results show that this approach can successfully predict human preference patterns. Synthetic speech using weights determined in this manner consistently demonstrates smoother transitions and higher voice quality than speech using manually set weights.