  We present a neural text-to-speech (TTS) method that models natural vocal
effort variation to improve the intelligibility of synthetic speech in the
presence of noise. The method consists of first measuring the spectral tilt of
unlabeled conventional speech data, and then conditioning a neural TTS model
with normalized spectral tilt among other prosodic factors. Changing the
spectral tilt parameter and keeping other prosodic factors unchanged enables
effective vocal effort control at synthesis time independent of other prosodic
factors. By extrapolation of the spectral tilt values beyond what has been seen
in the original data, we can generate speech with high vocal effort levels,
thus improving the intelligibility of speech in the presence of masking noise.
We evaluate the intelligibility and quality of normal speech and speech with
increased vocal effort in the presence of various masking noise conditions, and
compare these to well-known speech intelligibility-enhancing algorithms. The
evaluations show that the proposed method can improve the intelligibility of
synthetic speech with little loss in speech quality.
