Comparison of Speech Representations for Automatic Quality Estimation in Multi-Speaker Text-to-Speech Synthesis
Table 2: Speaker-level prediction results on Ophelia/LibriTTS synthetic speech. The performance shown here is based on models that were originally trained on the LA dataset (as reported in Table 1) before being used to evaluate our multi-speaker TTS from Ophelia/LibriTTS.
['[EMPTY]', '[BOLD] Metric', '[BOLD] Representations Pre-trained (VC-CNN)', '[BOLD] Representations Re-trained (LA-CNN)', '[BOLD] Representations xvec0', '[BOLD] Representations xvec1', '[BOLD] Representations xvec2', '[BOLD] Representations xvec3', '[BOLD] Representations xvec4', '[BOLD] Representations  [BOLD] xvec5', '[BOLD] Representations DS', '[BOLD] Representations AM']
Speaker analysis, which had not been done previously, has allowed us to examine the MOS prediction task from a new angle. The ability to rank speakers is potentially very useful for numerous development and evaluation tasks. We identified two TTS systems from the LA dataset which the pre-trained VC-CNN model performed best, and not surprisingly one of them is a similar architecture to our Ophelia TTS (A11: encoder-decoder using sequence-to-sequence modeling with attention). We identified system A12 from the LA dataset which had the most similar performance to our Opehlia/LibriTTS. We further observed that for both of these TTS systems, our NN could rank best/worst speakers, though the ability to rank could be greatly improved for both. Also importantly, we noted that for the true MOS labels, our Ophelia/TTS system could produce speakers who are similar quality to the WaveNetTTS speakers. Of course, these two systems underwent different MOS tests at different times, therefore we believe a new approach to scoring is a needed research direction.