# MixDiff-TTS: Mixture Alignment and Diffusion Model for Text-to-Speech
## 3 Results
### Evaluation
#### 3.3.2 Experimental Results  
In the evaluation experiments, we use a logarithmic method to calculate the \(F_{0}\) RMSE. To maintain fairness in assessing the MCD and \(F_{0}\) RMSE, we employ Dynamic Time Warping (DTW) [40] to establish alignment between synthesized speech and real speech samples. All evaluation results are summarized in Table 1. Compared to baseline models, MixDiff-TTS exhibits leading performance in SSIM and MCD assessments. However, its \(F_{0}\) RMSE is slightly higher than some baselines, indicating room for improvement in fundamental frequency prediction. These findings indicate that MixDiff-TTS demonstrates satisfactory performance in phoneme alignment and can synthesize high-fidelity audio. In the FAD evaluation, MixDiff-TTS outperforms other models. This demonstrates that its synthesized audio is highly similar to real audio, with their feature distributions closely aligned. \"Params\" refers to the total number of model parameters. We observe that MixDiff-TTS still has room for improvement in optimizing the parameter count. Additionally, to investigate the real-time synthesis performance of MixDiff-TTS, we selected 10 synthesized audio samples for RTF evaluation. These samples contain between 10 and 20 words. An RTF value less than 1 indicates that MixDiff-TTS has a fast inference speed and high real-time performance.  
We set the confidence interval for the MOS evaluation at 95%. To ensure the statistical reliability of subjective evaluation results, each sample is rated by at least 10 participants. All participants wear headphones and complete all evaluations in a quiet environment. Prior to the test, we shuffle the order of all samples to ensure that participants are unaware of which model generated each sample. We instruct participants to rate the audio based on pronunciation accuracy and naturalness using a 5-point scale ranging from 1 to 5, with 0.5-point increments between scale points. As shown in Table 1, MixDiff-TTS achieves a MOS score of 3.95, outperforming FastSpeech2 and DiffSpeech while slightly trailing PortaSpeech. This result demonstrates that MixDiff-TTS effectively models phoneme alignment and can synthesize audio with high pronunciation accuracy and naturalness. Furthermore, we use the CMOS metric to compare the performance among the models involved in the test. With FastSpeech2 as the baseline, participants evaluate speech samples generated by comparative models. As can be observed, MixDiff-TTS performs comparably to PortaSpeech and outperforms DiffSpeech, indicating that the model can generate higher-quality synthesized speech.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Model** & **SSIM** & **MCD** & \(\mathbf{F_{0}}\) **RMSE** & **FAD** & **Params** & **RTF** & **MOS** & **CMOS** \\ \hline Ground Truth & & & & & & 4.37 \(\pm\) 0.08 & \\ FastSpeech2 & 0.496 & 6.751 & 0.329 & 2.1460 & 35.16M & 0.1486 & 3.82 \(\pm\) 0.09 & 0.000 \\ PortaSpeech & 0.505 & 6.683 & 0.323 & 1.8593 & 23.97M & 0.1364 & 3.98 \(\pm\) 0.07 & 0.189 \\ DiffSpeech & 0.501 & 6.735 & 0.335 & 1.5165 & 45.11M & 0.1456 & 3.92 \(\pm\) 0.08 & 0.179 \\ MixDiff-TTS & 0.507 & 6.652 & 0.337 & 1.5013 & 46.31M & 0.1391 & 3.95 \(\pm\) 0.08 & 0.185 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of objective and subjective evaluations and model efficiency tests.