# Research on a Mongolian Text to Speech Model Based on Ghost and ILPCnet
## 4 Data Experiment
### Data Test for the Model and Results
#### 4.5.3 Real-Time Rate Analysis  
When the WaveNet vocoder synthesized speech, the speed of its speech synthesis was limited due to its reliance on the audio sample points generated in the previous step, resulting in a reduction in the speech quality [27]. In the experiment, the real-time rate of the speech synthesis between the Ghost-ILPCnet model and the improved Para-WaveNet model was compared. The real-time rate of speech synthesis was used to measure the performance of the speech synthesis model. The smaller the RTF value, the better the real-time rate of speech generated by the model and the better the interactivity. The results are shown in Table 8.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **RTF** \\ \hline Mel + WaveNet & 0.2412 \\ ParaNet & 0.1886 \\ Improved Para-WaveNet & 0.0859 \\ Transformer & 0.9141 \\ Tacto2 & 0.9411 \\ Mel + ILPCnet & 0.1921 \\ Ghost-ILPCnet (without Bang pre-training) & 0.0021 \\ Ghost-ILPCnet (Bang pre-training) & **0.0041** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Real-time factors for each model.  
In terms of vocoder, the Mel Spectrum + WaveNet Vocoder RTF value was 0.2412 and Mel Spectrum + ILPcnet was 0.1921. The experiments show that, under the same conditions, the real-time rate of the ILPcnet vocoder converting Mel spectrum into a speech waveform is 0.0491 faster than WaveNet. Therefore, introducing ILPcnet as the vocoder of the Ghost acoustic model can improve the synthesis speed of the entire system to a certain extent and meet the real-time requirements of the speech synthesis system.  
Observed from the aspect of using algorithm distillation, comparing the ParaNet model and the improved Para-WaveNet model, without any complex probability density distillation, the improved Para-WaveNet acoustic model generates Mongolian speech with a real-time rate of 0.0859, 0.1027 faster than the improved ParaNet model. Therefore, the real-time performance of speech generated by the improved Para-WaveNet acoustic model was optimized to a great extent.  
We observed whether or not non-autoregressive sequence algorithms were used and compared the more popular Transformer, Tactron2, ParaNet series models, and Ghost-ILPCnet. Since both Transformer and Tactron2 are models based on autoregressive sequences, their real-time rates for synthesizing Mongolian speech are both less than 1, and the fastest real-time rate is 0.9141, which basically meets the requirements for real-time speech synthesis. However, compared with models based on non-autoregressive sequences, the slowest non-autoregressive model generates speech at a real-time rate of 0.1886, and the speed of non-autoregressive models generating speech is more advanced.  
Finally, we tested whether adding Bang pre-training to the model affected the real-time rate of speech generated by the model. When the pre-trained model was added to the Mongolian speech synthesis model based on Ghost-ILPCnet, the real-time rate was 0.0041, which was only 0.002 slower than without adding it. However, the real-time rate of this model was still very fast among the non-autoregressive models.