Using conditional random fields to predict pitch accents in conversational speech

The detection of prosodic characteristics is an important aspect of both speech synthesis and speech recognition. Correct placement of pitch accents aids in more natural sounding speech, while automatic detection of accents can contribute to better word-level recognition and better textual understanding. In this paper we investigate probabilistic, contextual, and phonological factors that influence pitch accent placement in natural, conversational speech in a sequence labeling setting. We introduce Conditional Random Fields (CRFs) to pitch accent prediction task in order to incorporate these factors efficiently in a sequence model. We demonstrate the usefulness and the incremental effect of these factors in a sequence model by performing experiments on hand labeled data from the Switchboard Corpus. Our model outperforms the baseline and previous models of pitch accent prediction on the Switchboard Corpus.