AdaDurIAN: Few-shot Adaptation for Neural Text-to-Speech with DurIAN
Table 2: Robust mean opinion score (MOS) as a function of the amount of training data and different speakers. CN speaker denotes a native Chinese speaker, while EN speaker denotes a native English speaker. All audios are converted by Griffin-Lim algorithm [Griffin1984SignalEF] from mel spectrogtam.
['[BOLD] System', '[BOLD] Chinese sentences CN speaker', '[BOLD] Chinese sentences EN speaker', '[BOLD] English sentences CN speaker', '[BOLD] English sentences EN speaker', '[BOLD] Recordings CN speaker', '[BOLD] Recordings EN speaker']
Especially, CN speaker has a MOS 4.21 when given only 3 minutes of training data and there is only a MOS gap of 0.2 compared with MOS of recordings. Given just 1 minute of data, CN speaker with AdaDurIAN system can still get a MOS 4.14 on Chinese sentences and 4.13 on English sentences, respectively. Although EN speaker with AdaDurIAN has a decreasing MOS with the increase of data on Chinese sentences, the highest MOS 3.90 only has a minor distance to the MOS 4.14 of the recordings. This indicates that AdaDurIAN can generalize well in cross-lingual speaking even if few minutes of monolingual data is given.