Analysis of speaker similarity in the statistical speech synthesis systems using a hybrid approach

Statistical speech synthesis (SSS) approach has become one of the most popular and successful methods in the speech synthesis field. Smooth speech transitions, without the spurious errors that are observed in unit selection systems, can be generated with the SSS approach. However, a well-known issue with SSS is the lack of voice similarity to the target speaker. The issue arises both in speaker-dependent models and models that are adapted from average voices. Moreover, in speaker adaptation, similarity to the target speaker does not increase significantly after around one minute of adaptation data which potentially indicates inherent bottleneck(s) in the system. Here, we propose using the hybrid speech synthesis approach to understand the key factors behind the speaker similarity problem. To that end, we try to answer the following question: which segments and parameters of speech, if generated/synthesized better, would have a substantial improvement on speaker similarity? In this work, our hybrid methods are described and listening test results are presented and discussed.