Investigating the Relation Between Voice Corpus Design and Hybrid Synthesis Under Reduction Constraint.

Hybrid TTS systems generally try to optimise their cost function with the voice provided to generate the best signal. The voice is based on a speech corpus usually designed for a specific purpose. In this paper, we consider that the voice creation is realized through a corpus design step under reduction constraints. During this stage, a recording script is crafted to be optimal for the target TTS engine and its purpose. In this paper, we investigate the impact of sharing information between the corpus design step and the hybrid TTS optimisation step. We start from a reduced voice optimized for a unit selection system using a CNN-based model. This baseline is compared to a hybrid TTS system that uses, as its target cost, a linguistic embedding built for the recording script design step. This approach is also compared to a standard hybrid TTS system trained only on the voice and so that does not have information about the corpus design process. Objective measures and perceptual evaluations show how the integration of the corpus design embedding as target cost outperforms a classical hard-coded target cost. However, the feed-forward DNN acoustic model from the standard hybrid TTS system remains the best. This emphasizes the importance of acoustic information in the TTS target cost, which is not directly available before the voice recording.