Automatic ToBI prediction and alignment to speed manual labeling of prosody

Abstract

ToBI (Tones and Break Indices) prosodic labeling of a speech corpus is a slow, labor-intensive process that typically takes from 100 to 300 times real time, even with experienced labelers. An experiment was conducted to determine: (1) whether manual correction of automatically assigned ToBI labels would speed up the labeling process; and (2) whether default labels introduced any bias in label assignment. A group of four graduate linguistics students previously trained in ToBI labeling were paid participants in the study. A large speech corpus of one female speaker reading several types of texts was labeled over a period of nine months. Half of each recording was labeled in the normal fashion ‘‘from scratch’’ without default labels, and the other half was presented with preassigned default labels for labelers to correct. Default ToBI labels were predicted from text using techniques developed for text-to-speech synthesis. Both labeling methods used standard Entropic waves+ tools developed for ToBI transcription. A log file was created during the labeling of each file that identified labeler, filename, method, and beginning and ending times of each session. Results indicate that labeling from defaults was faster than standard labeling, and that defaults had relatively little impact on label assignment.