  Recent advancements in deep learning led to human-level performance in
single-speaker speech synthesis. However, there are still limitations in terms
of speech quality when generalizing those systems into multiple-speaker models
especially for unseen speakers and unseen recording qualities. For instance,
conventional neural vocoders are adjusted to the training speaker and have poor
generalization capabilities to unseen speakers. In this work, we propose a
variant of WaveRNN, referred to as speaker conditional WaveRNN (SC-WaveRNN). We
target towards the development of an efficient universal vocoder even for
unseen speakers and recording conditions. In contrast to standard WaveRNN,
SC-WaveRNN exploits additional information given in the form of speaker
embeddings. Using publicly-available data for training, SC-WaveRNN achieves
significantly better performance over baseline WaveRNN on both subjective and
objective metrics. In MOS, SC-WaveRNN achieves an improvement of about 23% for
seen speaker and seen recording condition and up to 95% for unseen speaker and
unseen condition. Finally, we extend our work by implementing a multi-speaker
text-to-speech (TTS) synthesis similar to zero-shot speaker adaptation. In
terms of performance, our system has been preferred over the baseline TTS
system by 60% over 15.5% and by 60.9% over 32.6%, for seen and unseen speakers,
respectively.
