  Spontaneous speech has many affective and pragmatic functions that are
interesting and challenging to model in TTS. However, the presence of reduced
articulation, fillers, repetitions, and other disfluencies in spontaneous
speech make the text and acoustics less aligned than in read speech, which is
problematic for attention-based TTS. We propose a TTS architecture that can
rapidly learn to speak from small and irregular datasets, while also
reproducing the diversity of expressive phenomena present in spontaneous
speech. Specifically, we add utterance-level prosody control to an existing
neural HMM-based TTS system which is capable of stable, monotonic alignments
for spontaneous speech. We objectively evaluate control accuracy and perform
perceptual tests that demonstrate that prosody control does not degrade
synthesis quality. To exemplify the power of combining prosody control and
ecologically valid data for reproducing intricate spontaneous speech phenomena,
we evaluate the system's capability of synthesizing two types of creaky voice.
Audio samples are available at
https://www.speech.kth.se/tts-demos/prosodic-hmm/
