HIGH-ACOUSTIC FIDELITY TEXT TO SPEECH SYNTHESIS WITH FINE-GRAINED CONTROL OF SPEECH ATTRIBUTES

Recently developed neural-based TTS models have focused on robustness and finer control over acoustic features such as phoneme duration, energy, and f0, allowing users to have some degree of control over the prosody of the generated speech. We propose a model with fine grained attribute control, which also has better acoustic fidelity (attributes of the output which we want to control do not deviate from the control signals) than previously proposed models as shown in our experiments. Unlike other models, our proposed model does not require fine-tuning the vocoder on its outputs, indicating that it generates higher quality mel-spectrograms that are closer to the ground-truth distribution than that of other models.