Carnegie Mellon University THE ROBOTICS INSTITUTE

Thesis DefenseKevin A. Lenzo

Friday, December 9, 2016 GHC 65019:30 a.m.

Alan W. Black Chair

Jack Mostow

Alex Rudnicky

Julia Hirschberg Columbia University

Thesis Committee

Improving Prosody through Analysis by Synthesis

Abstract An   itera)ve  model-­‐based  method   is  proposed   for   improving   linguis)c   structure,   segmenta)on,  and  prosodic  annota)ons  that  correspond  to  the  delivery  of  each  u:erance  as  regularized  across  the   data.   For   each   itera)on,   the   training   u:erances   are   resynthized   according   to   the   exis)ng  symbolic  annota)on.  Values  of  various  features  and  subgraph  structures  are  "twiddled:"  each  is  perturbed   based   on   the   features   and   constraints   of   the   model.   Twiddled   u:erances   are  evaluated   using   an   objec)ve   func)on   appropriate   to   the   type   of   perturba)on   and   compared  with   the  unmodified,   resynthesized  u:erance.   The   instance  with   least   error   is   assigned  as   the  current   annota)on,   and   the   en)re   process   is   repeated.   At   each   itera)on,   the   model   is   re-­‐es)mated,   and   the  distribu)ons  and  annota)ons   regularize  across   the   corpus.  As  a   result,   the  annota)ons  have  more  accurate  and  effec)ve  distribu)ons,  which  leads  to  improved  control  and  expressiveness  given  the  features  of  the  model.