# Using Transfer Learning to Realize Low Resource Dungan Language Speech Synthesis
## 3 Results
### Evaluation on Transfer Learning-Based Dungan Acoustic Models
#### 3.2.4 Subjective Evaluation  
For subjective evaluations, 30 sentences were randomly selected from the test set. We conducted three tests: mean opinion score (MOS), degradation mean opinion score (DMOS), and AB preference to assess the quality of synthesized speech. We recruited 20 native Mandarin speakers and 10 native Dungan international students (who understood Chinese) as participants. These participants received training before the formal evaluation. Mandarin participants evaluated the Mandarin acoustic models of MSD and MDSM, whereas Dungan participants assessed the Dungan acoustic models of DSD and MDSD. During the MOS test, participants rated the naturalness of synthesized speech on a 5-point scale. The average MOS scores for synthesized Dungan and Mandarin speech are presented in Figures 7 and 8.  
In the DMOS test, each model's synthesized utterance and corresponding original recording comprised a pair of speech files. These pairs were randomly played to the subjects, with the synthesized speech preceding the original. The participants were tasked with meticulously comparing the two files and rating the similarity of the synthesized speech to the original on a 5-point scale. A score of 5 indicated that the synthesized speech was similar to the original, whereas a score of 1 signified a significant disparity. Figures 9 and 10 display the average DMOS scores for synthesized Dungan and Mandarin speech, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Tacotron+Griffin-Lim** & **Tacotron2+WaveNet** & **Tacotron2+WaveRNN** \\ \hline MCD (dB) & 7.523 & 7.419 & 7.395 \\ BAP (dB) & 0.178 & 0.175 & 0.174 \\ F0 RMSE (Hz) & 26.891 & 26.753 & 26.617 \\ V/UV (\%) & 7.774 & 7.693 & 7.607 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Objective results of MDSD acoustic model for Dungan.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Tacotron+Griffin-Lim** & **Tacotron2+WaveNet** & **Tacotron2+WaveRNN** \\ \hline MCD(dB) & 5.339 & 5.241 & 5.108 \\ BAP (dB) & 0.174 & 0.173 & 0.171 \\ F0 RMSE (Hz) & 13.775 & 13.326 & 13.092 \\ V/UV (\%) & 5.542 & 5.472 & 5.481 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Objective results of MDSM acoustic model for Mandarin.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **IR (\%)** & **DFR (\%)** \\ \hline DSD-Tacotron+Griffin-Lim & 82.93 & 79.64 \\ DSD-Tacotron2+WaveNet & 86.67 & 82.43 \\ DSD-Tacotron2+WaveRNN & 89.41 & 84.39 \\ MDSD-Tacotron+Griffin-Lim & 95.03 & 91.14 \\ MDSD-Tacotron2+WaveNet & 96.69 & 94.43 \\ MDSD-Tacotron2+WaveRNN & 98.47 & 97.39 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Readability of synthesized Dungan speech.  
Figure 8: The average MOS scores of synthesized Mandarin speech under 95% confidence intervals.  
Figure 7: The average MOS scores of synthesized Dungan speech under 95% confidence intervals.  
Figure 9: The average DMOS scores of synthesized Dungan speech under 95% confidence intervals.  
particularly regarding the naturalness and readability of synthesized speech. With the addition of the Mandarin corpus, the quality and readability of synthesized Dungan speech using the transfer learning-based TTS frameworks surpass those monolingual corpus-trained TTS frameworks. This is further validated by the AB preference test, which confirms that our proposed TTS frameworks offer improved quality and readability compared to the speech synthesized by the monolingual acoustic model.