In Other News: A Bi-style Text-to-speech Modelfor Synthesizing Newscaster Voice with Limited Data
Tablica 1: Analysis of mean prosodic variations based on lf0 per utterance
['[BOLD] Corpus', '[BOLD] Variance', '[BOLD] Range']
This section aims at understanding the prosodic variability in neutral-style, and newscaster-style corpora. For this purpose, we study the average variance in the natural logarithm of fundamental frequency (lf0) for each utterance in the two styles. For contrast, we also study per-utterance lf0 in a mixed-expressive corpus from the same speaker. We notice that among the three corpora, the neutral-style utterances have the lowest mean variance per utterance, making it more tractable and easier to model with NTTS than the other two corpora. Newscaster-style has a slightly higher mean variance given greater expressiveness, and the mixed-expressive corpus has the highest mean variance. Latent-variable models Akuzawa et al. ; Hsu et al. As we have already discussed, these models are slow to train, and require prediction or manual injection of continuous latent variables during inference. These might not be well-suited for the task of modelling newscaster-style, which even though is expressive, has much lower mean variance per utterance than the mixed-expressive corpus.