  Parallel text-to-speech (TTS) models have recently enabled fast and
highly-natural speech synthesis. However, they typically require external
alignment models, which are not necessarily optimized for the decoder as they
are not jointly trained. In this paper, we propose a differentiable duration
method for learning monotonic alignments between input and output sequences.
Our method is based on a soft-duration mechanism that optimizes a stochastic
process in expectation. Using this differentiable duration method, we introduce
AutoTTS, a direct text-to-waveform speech synthesis model. AutoTTS enables
high-fidelity speech synthesis through a combination of adversarial training
and matching the total ground-truth duration. Experimental results show that
our model obtains competitive results while enjoying a much simpler training
pipeline. Audio samples are available online.
