Deep Voice 2: Multi-Speaker Neural Text-to-Speech
Table 1: Mean Opinion Score (MOS) evaluations with 95% confidence intervals of Deep Voice 1, Deep Voice 2, and Tacotron. Using the crowdMOS toolkit, batches of samples from these models were presented to raters on Mechanical Turk. Since batches contained samples from all models, the experiment naturally induces a comparison between the models.
['[BOLD] Model', '[BOLD] Samp. Freq.', '[BOLD] MOS']
We train Deep Voice 1, Deep Voice 2, and Tacotron on an internal English speech database containing approximately 20 hours of single-speaker data. The results show conclusively that the architecture improvements in Deep Voice 2 yield significant gains in quality over Deep Voice 1. They also demonstrate that converting Tacotron-generated spectrograms to audio using WaveNet is preferable to using the iterative Griffin-Lim algorithm.