Comparison of Speech Representations for Automatic Quality Estimation in Multi-Speaker Text-to-Speech Synthesis
Table 1: System-level and speaker-level prediction results on the LA dataset, comparing original pre-trained VC-CNN model, re-trained LA-CNN model, and 8 new representations from best parameters on our CNN architecture. For each, the best model and parameters were selected based on highest speaker-level SRCC score (yellow).
['[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']
For each feature representation, the best model and parameters were selected based on highest speaker-Level SRCC score (yellow). We also highlight the system-level SRCC score (pink). We identified the overall best representation as xvec5 (blue) because of the high SRCC scores at both system-level and speaker-level, as well as the very high Kendall tau score for ranking speakers. We further examined the ranking distributions for individual speakers per-system. From the ground-truth MOS scores, we focused on the following two systems: A08: poorest quality system, HMM-based TTS (MOSmean=1.79) A10: