  Most state-of-the-art Text-to-Speech systems use the mel-spectrogram as an
intermediate representation, to decompose the task into acoustic modelling and
waveform generation.
  A mel-spectrogram is extracted from the waveform by a simple, fast DSP
operation, but generating a high-quality waveform from a mel-spectrogram
requires computationally expensive machine learning: a neural vocoder. Our
proposed ``autovocoder'' reverses this arrangement. We use machine learning to
obtain a representation that replaces the mel-spectrogram, and that can be
inverted back to a waveform using simple, fast operations including a
differentiable implementation of the inverse STFT.
  The autovocoder generates a waveform 5 times faster than the DSP-based
Griffin-Lim algorithm, and 14 times faster than the neural vocoder HiFi-GAN. We
provide perceptual listening test results to confirm that the speech is of
comparable quality to HiFi-GAN in the copy synthesis task.
