# MixDiff-TTS: Mixture Alignment and Diffusion Model for Text-to-Speech
## 3 Results
### Ablation Studies  
To investigate the effectiveness of the mixture alignment scheme, we conduct comparative experiments between it and phoneme-level hard alignment methods. \"MixDiff-TTS--phoneme-level hard alignment\" indicates that MixDiff-TTS uses phoneme-level hard alignment. In Table 2, MixDiff-TTS with the mixture alignment mechanism demonstrates superior performance over its phoneme-level hard alignment counterpart in both MOS and CMOS scores, thereby indicating that this mechanism significantly enhances the prosodic naturalness of synthesized speech.  
To evaluate the impact of the MixDiff-TTS architecture on speech generation, we conduct ablation studies on the W2P-RelPosAttention module, pre-net, post-net, and residual network. Each ablation study is evaluated based on SSIM, MCD, \(F_{0}\) RMSE, MOS, and CMOS. \"MixDiff-TTS--W2P-RelPosAttention\" refers to the MixDiff-TTS model after removing the W2P-RelPosAttention module. In Table 3, MixDiff-TTS with the W2P-RelPosAttention module achieves the highest SSIM and lowest MCD scores, whereas its \(F_{0}\) RMSE performance remains suboptimal. The MOS and CMOS results indicate that MixDiff-TTS outperforms its variant without the W2P-RelPosAttention module. The experimental results suggest that MixDiff-TTS becomes more competitive in handling long-sequence text after incorporating the W2P-RelPosAttention module.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Setting** & **MOS** & **CMOS** \\ \hline MixDiff-TTS & 3.95 \(\pm\) 0.08 & 0.000 \\ MixDiff-TTS—phoneme-level hard alignment & 3.83 \(\pm\) 0.07 & \(-\)0.247 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The ablation study on mixture alignment.  
Figure 5: Comparison of mel-spectrograms from different TTS models and attention alignment of MixDiff-TTS. (**a**) Ground-truth mel-spectrogram. (**b**) Mel-spectrogram predicted by FastSpeech2. (**c**) Mel-spectrogram predicted by PortaSpeech. (**d**) Mel-spectrogram predicted by DiffSpeech. (**e**) Mel-spectrogram predicted by MixDiff-TTS. (**f**) Attention alignment of MixDiff-TTS.  
\"MixDiff-TTS--pre-net\" refers to the MixDiff-TTS model after removing the pre-net structure. As shown in Table 4, removing the pre-net results in degraded performance in SSIM, MCD, \(F_{0}\) RMSE, MOS, and CMOS for MixDiff-TTS. Experimental results demonstrate that the pre-net significantly enhances the model's capacity for learning the speech synthesis task. In Table 5, \"MixDiff-TTS--residual network\" denotes the MixDiff-TTS variant that directly outputs post-net processed results after removing the residual network. \"MixDiff-TTS--post-net\" denotes the MixDiff-TTS model after removing the post-net structure. The findings indicate that MixDiff-TTS with the post-net and residual network achieves the best performance in SSIM and MCD, although it does not show an improvement in \(F_{0}\) RMSE. In subjective evaluation, MixDiff-TTS without the post-net and residual network exhibits significantly poorer performance than the baseline model retaining these components. These experimental findings demonstrate that the inclusion of the post-net significantly boosts MixDiff-TTS's capacity for mel-spectrogram reconstruction.