# Research on a Mongolian Text to Speech Model Based on Ghost and ILPCnet
## 4 Data Experiment
### Data Test for the Model and Results
#### 4.5.2 Word Error Rate Analysis  
In addition, in the sequence-to-sequence model, the encoder-decoder module can cause a mismatch between the phoneme sequence and the mel spectrum during the training process, resulting in unstable factors, such as wrong words and missing words, in the synthesized speech. In order to test the word error rate of the Mongolian speech synthesis model, this study selected 30 Mongolian sentences and tested them under the same experimental conditions. Five listeners counted the number of missing words and wrong words in the test speech set and averaged these data to obtain the final results of the training model, as shown in Table 7. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **STFT Loss** & **Missing Words** & **Wrong Words** & **Error Rate** \\ \hline ParaNet (character) & & 11 & 11 & 60\% \\ ParaNet (phoneme) & & 10 & 10 & 53\% \\ Para-WaveNet (character) & \(L_{\text{S}}^{(1)}\) & 13 & 13 & 66\% \\ Para-WaveNet (phoneme) & \(L_{\text{S}}^{(1)}\) & 10 & 10 & 56\% \\ Improved Para-WaveNet & \(L_{\text{s}}^{(1)}+L_{\text{s}}^{(2)}+L_{\text{s}}^{(3)}\) & 9 & 9 & 53\% \\ (phoneme) & \(L_{\text{S}}^{(1)}\) & 3 & 3 & 17\% \\ Transfomer (phoneme) & \(L_{\text{s}}^{(1)}+L_{\text{s}}^{(2)}+L_{\text{s}}^{(3)}\) & 2 & 2 & 10\% \\ Ghost-ILPCnet (phoneme) & \(L_{\text{s}}^{(1)}+L_{\text{s}}^{(2)}+L_{\text{s}}^{(3)}\) & 1 & 1 & 10\% \\ (without Bang pre-training) & \(L_{\text{s}}^{(1)}+L_{\text{s}}^{(2)}+L_{\text{s}}^{(3)}\) & **1** & **1** & **6\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the word error rates of each model in 30 sentences. Figure 9: Loss curve of Ghost-ILPCnet model. From the perspective of the impact of the input on model results, using phoneme sequences as the model input can reduce the error rate of the model to varying degrees and improve the accuracy of the model's synthesized speech. Therefore, the subsequent models all used phoneme sequences as the model input. From the perspective of the impact of multi-resolution STFT loss on the model results, the Para-WaveNet (phoneme) improved by multi-resolution STFT loss had 9 sentences with missing words, 7 sentences with errors, and an error rate of 53%. The fixed STFT loss-assisted improved Para-WaveNet (phoneme) had 10 missing words and 7 incorrect sentences, with an error rate of 56%. Under the same conditions, the error rate of speech generated by the model was reduced by 3%. Then, the experiment introduced a Transformer-based Mongolian speech synthesis model for comparison. After the experimental verification, the Transformer model (phoneme) assisted by a multi-resolution STFT loss generated a speech error rate of 10% and was assisted by a single fixed STFT loss of 17%. The error rate of the model decreased by 7%. It was concluded that the improved Para-WaveNet model assisted by the multi-resolution STFT loss proposed in this article could stably generate high-definition Mongolian speech, which improved the quality of the generated speech. From the perspective of the impact of adding the Bang pre-training model on the experimental results, the Ghost-ILPCnet model (phoneme) added to the Bang pre-training model generated 1 sentence with missing words and 1 sentence with the wrong number of words in the speech. The error rate was only 6%. The Ghost-ILPCnet model (phoneme) without adding the Bang pre-training model generated 1 sentence with missing words in the speech and 2 sentences with the number of errors. The error rate was 10% and the error rate of the model differed by 4%. By adding a Bang-based pre-training mechanism for phoneme information, context information can be used to more accurately generate speech signals in parallel, improving the quality of the speech generated by the Mongolian speech synthesis model.