  By representing speaker characteristic as a single fixed-length vector
extracted solely from speech, we can train a neural multi-speaker speech
synthesis model by conditioning the model on those vectors. This model can also
be adapted to unseen speakers regardless of whether the transcript of
adaptation data is available or not. However, this setup restricts the speaker
component to just a single bias vector, which in turn limits the performance of
adaptation process. In this study, we propose a novel speech synthesis model,
which can be adapted to unseen speakers by fine-tuning part of or all of the
network using either transcribed or untranscribed speech. Our methodology
essentially consists of two steps: first, we split the conventional acoustic
model into a speaker-independent (SI) linguistic encoder and a speaker-adaptive
(SA) acoustic decoder; second, we train an auxiliary acoustic encoder that can
be used as a substitute for the linguistic encoder whenever linguistic features
are unobtainable. The results of objective and subjective evaluations show that
adaptation using either transcribed or untranscribed speech with our
methodology achieved a reasonable level of performance with an extremely
limited amount of data and greatly improved performance with more data.
Surprisingly, adaptation with untranscribed speech surpassed the transcribed
counterpart in the subjective test, which reveals the limitations of the
conventional acoustic model and hints at potential directions for improvements.
