A Synthesis Instance Pruning Approach Based on Virtual Non-uniform Replacements

The employment of non-uniform processes assists greatly in the corpus-based text-to-speech (TTS) system to synthesize natural speech. However, tailoring a TTS voice font, or pruning redundant synthesis instances, usually results in loss of non-uniform synthesis instances. In order to solve this problem, we propose the concept of virtual non-uniform instances. According to this concept and the synthesis frequency of each instance, the algorithm named StaRp-VPA is constructed to make up for the loss of nonuniform instances. In experimental testing, the naturalness scored by the mean opinion score (MOS) remains almost unchanged when less than 50% instances are pruned, and the MOS is only slightly degraded for reduction rates above 50%. The test results show that the algorithm StaRp-VPA is effective.