  Whispered speech is characterised by a noise-like excitation that results in
the lack of fundamental frequency. Considering that prosodic phenomena such as
intonation are perceived through f0 variation, the perception of whispered
prosody is relatively difficult. At the same time, studies have shown that
speakers do attempt to produce intonation when whispering and that prosodic
variability is being transmitted, suggesting that intonation "survives" in
whispered formant structure. In this paper, we aim to estimate the way in which
formant contours correlate with an "implicit" pitch contour in whisper, using a
machine learning model. We propose a two-step method: using a parallel corpus,
we first transform the whispered formants into their phonated equivalents using
a denoising autoencoder. We then analyse the formant contours to predict
phonated pitch contour variation. We observe that our method is effective in
establishing a relationship between whispered and phonated formants and in
uncovering implicit pitch contours in whisper.
