MultiSpeech: Multi-Speaker Text to Speech with Transformer
Table 2: The MOS with 95% confidence intervals and diagonal attention rate r of the ablation study on VCTK. −DC means not using diagonal constraint during training and inference. −LN means using x+p as encoder input but not LN(x)+p. −PB means using pre-net structure like 80-256-256-256 instead of our proposed 80-32-32-256.
['[BOLD] Setting', '[BOLD] MOS', '[BOLD] r']
Ablation Study. We first conduct ablation study on VCTK dataset to verify the effectiveness of each proposed technique: diagonal constraint (DC) in attention, layer normalization (LN) in encoder, pre-net bottleneck (PB) in decoder. After removing diagonal constraint (DC), layer normalization (LN) and pre-net bottleneck (PB) respectively, both MOS score and diagonal rate r drop. After further removing all the three techniques (-DC-LN-PB, i.e., Transformer based TTS), both MOS and r drop largely. These ablation studies verify the effectiveness of the three techniques to improve attention alignments for better voice quality.