# Meta-Learning for Mandarin-Tibetan Cross-Lingual Speech Synthesis
## 6 Evaluations and Analysis
### Cross-Lingual Speech Synthesis Evaluation  
We build a small-scale dataset containing 50 bilingual sentences for cross-lingual speech synthesis evaluation. Because seq2 seq-M and seq2 seq-T are monolingual TTS, the speech samples are only generated by the shared-MT, separate-MT, and meta-MT models in Mandarin speaker's and Tibetan speaker's voices, respectively. We also randomly selected 20 synthesized speech samples to conduct MOS and AB preference tests for speech naturalness and cross-language speaker similarity preservation of the proposed TTS models. In addition, 20 bilingual speakers in Mandarin and Tibetan were invited to participate in each test.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **seq2 seq-M** & **shared-MT** & **separate-MT** & **meta-MT** & **Neutral** \\ \hline
1 & 56.5 & 31.2 & - & - & 12.2 \\
2 & - & 37.8 & 49.8 & - & 12.5 \\
3 & - & - & 22.3 & 66.2 & 11.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Subjective AB preference score(%) of synthesized Mandarin speech samples with \(p<0.01\).  
Figure 6: The average MOS scores of synthesized Tibetan speech samples under 95% confidence intervals.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **seq2 seq-T** & **shared-MT** & **separate-MT** & **meta-MT** & **Neutral** \\ \hline
1 & 19.3 & 70.8 & - & - & 9.8 \\
2 & - & 27.3 & 56.7 & - & 15.3 \\
3 & - & - & 24.7 & 64.8 & 10.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Subjective AB preference score(%) of synthesized Tibetan speech samples with \(p<0.01\).  
In the MOS test, subjects were asked to rate the naturalness of the synthesized speech samples using a 5-point scale score. The average MOS scores of synthesized bilingual speech samples in Mandarin speaker's or Tibetan speaker's voices are shown in Figure 7. The MOS results of the baseline show that the separate-MT outperforms the shared-MT in cross-lingual speech synthesis, which indicates that the separate-MT benefits more from less language interference when encoding the inputs to hidden representations. Furthermore, the MOS results also show that our proposed meta-MT model can synthesize more natural speech than the baseline in bilingual sentences. Besides, the MOS results show that we can synthesize more natural bilingual speech in a Tibetan speaker's voice than in a Mandarin speaker's voice.  
In the AB preference test, the listeners were required to provide a speaker similarity choice among three options: (1) similar to the Mandarin speaker's voice; (2) no preference; (3) similar to the Tibetan speaker's voice. The AB preference test results are shown in Figure 8. Similar to the previous results, the separate-MT slightly outperforms the shared-MT in cross-lingual speech synthesis. Furthermore, the meta-MT outperforms baseline TTS  
Figure 8: Speaker similarity results of Mandarin speech samples in Tibetan speaker’s (TS) voice generated by shared-MT, separate-MT and meta-MT models, Tibetan speech samples in Mandarin speaker’s (MS) voice generated by shared-MT, separate-MT and meta-MT models. MS-GT and TS-GT are the ground truth of Mandarin speaker and Tibetan speaker, respectively. NP denotes no preference.  
Figure 7: The average MOS scores of synthesized bilingual speech samples in Mandarin speaker’s or Tibetan speaker’s voices under 95% confidence intervals.  
by successfully maintaining both the Tibetan speaker's voice and the Mandarin speaker's voice. Specifically, no models can maintain the Mandarin speaker's voice as well as the Tibetan speaker's voice, which may be partially due to the low naturalness of synthesized Mandarin speech.