In Other News: A Bi-style Text-to-speech Modelfor Synthesizing Newscaster Voice with Limited Data
Tablica 4: Listener ratings from the MUSHRA evaluation
['[BOLD] System', '[BOLD] Mean score', '[BOLD] Median score', '[BOLD] Mean Rank', '[BOLD] Median Rank']
The proposed model closes the gap between concatenative-based synthesis for newsreading, which is still largely the industry standard, and the natural recordings by 69.7%. The gap compared with the neutral NTTS voice is also closed by 60.9%. All of the systems present in the MUSHRA test are statistically significant from each other at a p-value of 0.01. This significance is observed across the listener responses using a t-test. Holm-Bonferroni correction was applied due to the number of condition pairs to compare. This significance is also observed over the MUSHRA responses in terms of the rank order awarded by listeners. For this a Wilcoxon signed-rank test applying Holm-Bonferroni correction was used.