  Traditional vocoder-based statistical parametric speech synthesis can be
advantageous in applications that require low computational complexity. Recent
neural vocoders, which can produce high naturalness, still cannot fulfill the
requirement of being real-time during synthesis. In this paper, we experiment
with our earlier continuous vocoder, in which the excitation is modeled with
two one-dimensional parameters: continuous F0 and Maximum Voiced Frequency. We
show on the data of 9 speakers that an average voice can be trained for
DNN-TTS, and speaker adaptation is feasible 400 utterances (about 14 minutes).
Objective experiments support that the quality of speaker adaptation with
Continuous Vocoder-based DNN-TTS is similar to the quality of the speaker
adaptation with a WORLD Vocoder-based baseline.
