Using Weakly Supervised Learning to Improve Prosodic Event Detection
by
Darby Wong
Prosody is the term used for the intonation, emphasis, and word grouping
that people use to aid in spoken language communication. We use the term
.prosodic event. to describe instances where there is a significant marker
of prosody, such as a prosodic phrase boundary, in speech. Prosodic
phrase boundaries are particularly important for allowing a listener to
parse speech into syntactic phrases which help decode sentence meaning.
The ultimate goal of our work is to enable the use of these cues in
automatic syntactic parsing.
Unfortunately, it is currently not possible to detect prosodic events in
conversational speech with high reliability, in part because there is
little data with prosodic annotation available for training models. To
address this problem, we explore the usage of weakly supervised learning
strategies. Several methods of weakly supervised learning are explored,
including cotraining and bagging. We present an analysis of the
results for each of these methods, as well as a discussion concerning the
tradeoffs between one another.
Advised by Mari Ostendorf
MGH 251
Wednesday
March 10, 2004
3:30 - 4:20 pm