Unsupervised Polyglot Text-to-Speech
Table 2: MOS similarity scores (Mean ± SE)
['[ITALIC] Source∖ [ITALIC] Target', 'English', 'Spanish', 'German']
In Tab. As can be seen, we get good results for MOS score quality (above 3.0). However, it should be noted that the mean MOS score on the original samples from these datasets was about 4.46. Following encoding and decoding with the world vocoder, this drops slightly to 4.19. In Tab. As can be seen for the baseline, we get good self-similarity for the English language. Moreover, for English language with polyglot synthesis, we get good MOS similarity scores results, i.e. above 3.40. Spanish and German obtains lower MOS similarity scores than English. We conjecture that this is a result of the fact that the VCTK dataset is larger then DIMEx100 and PhonDat1 (VCTK has ∼40k voice samples, DIMEx100 has ∼5.5k voice samples and PhonDat1 has ∼15k voice samples). For the Spanish and German languages, we get good results for self-similarity (above 3.15), and the Spanish to German (and vice versa) MOS scores polyglot synthesis results are close to the self-similarity MOS scores on these languages. In Tab. Without this term, the polyglot behavior arises only from the shared embedding. As can be seen, we get acceptable MOS similarity scores results (above 3.0). However, there is a noticeable gap in performance, in comparison to the results of the full system, as presented in Tab. In Tab. , we evaluate the contribution of the Lpoly term on the automatic identification accuracy of the generated sentences. As expected, the best identification results are obtained for the self-language synthesis sentences (located along the diagonal). The polyglot synthesis accuracy (off diagonal) is good, in terms of both Top1 and Top5 accuracy (above 70% and 91%, respectively). Where Top1 and Top5 refer to the accuracy of correctly classify the right speaker in the top one and the top five guesses, respectively. We can observe that adding Lpoly to the loss, improves the polyglot synthesis dramatically. For example, conversion from German to English improves in terms of Top1 accuracy from 82.71% to 92.29% when adding Lpoly. Moreover, we can see that adding it also gets better or comparable results for same-language synthesis.