  We describe speaker-independent speech synthesis driven by a small set of
phonetically meaningful speech parameters such as formant frequencies. The
intention is to leverage deep-learning advances to provide a highly realistic
signal generator that includes control affordances required for stimulus
creation in the speech sciences. Our approach turns input speech parameters
into predicted mel-spectrograms, which are rendered into waveforms by a
pre-trained neural vocoder. Experiments with WaveNet and HiFi-GAN confirm that
the method achieves our goals of accurate control over speech parameters
combined with high perceptual audio quality. We also find that the small set of
phonetically relevant speech parameters we use is sufficient to allow for
speaker-independent synthesis (a.k.a. universal vocoding).
