  In this paper, we propose an online speaker adaptation method for
WaveNet-based neural vocoders in order to improve their performance on
speaker-independent waveform generation. In this method, a speaker encoder is
first constructed using a large speaker-verification dataset which can extract
a speaker embedding vector from an utterance pronounced by an arbitrary
speaker. At the training stage, a speaker-aware WaveNet vocoder is then built
using a multi-speaker dataset which adopts both acoustic feature sequences and
speaker embedding vectors as conditions.At the generation stage, we first feed
the acoustic feature sequence from a test speaker into the speaker encoder to
obtain the speaker embedding vector of the utterance. Then, both the speaker
embedding vector and acoustic features pass the speaker-aware WaveNet vocoder
to reconstruct speech waveforms. Experimental results demonstrate that our
method can achieve a better objective and subjective performance on
reconstructing waveforms of unseen speakers than the conventional
speaker-independent WaveNet vocoder.
