  Disfluencies in spontaneous speech are known to be associated with prosodic
disruptions. However, most algorithms for disfluency detection use only word
transcripts. Integrating prosodic cues has proved difficult because of the many
sources of variability affecting the acoustic correlates. This paper introduces
a new approach to extracting acoustic-prosodic cues using text-based
distributional prediction of acoustic cues to derive vector z-score features
(innovations). We explore both early and late fusion techniques for integrating
text and prosody, showing gains over a high-accuracy text-only model.
