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.