  Converting input symbols to output audio in TTS requires modelling the
durations of speech sounds. Leading non-autoregressive (NAR) TTS models treat
duration modelling as a regression problem. The same utterance is then spoken
with identical timings every time, unlike when a human speaks. Probabilistic
models of duration have been proposed, but there is mixed evidence of their
benefits. However, prior studies generally only consider speech read aloud, and
ignore spontaneous speech, despite the latter being both a more common and a
more variable mode of speaking. We compare the effect of conventional
deterministic duration modelling to durations sampled from a powerful
probabilistic model based on conditional flow matching (OT-CFM), in three
different NAR TTS approaches: regression-based, deep generative, and
end-to-end. Across four different corpora, stochastic duration modelling
improves probabilistic NAR TTS approaches, especially for spontaneous speech.
Please see https://shivammehta25.github.io/prob_dur/ for audio and resources.
