An automatic prosody recognizer using a coupled multi-stream acoustic model and a syntactic-prosodic language model
Automatic detection and labeling of prosodic events in speech has received much attention from speech technologists and linguists ever since the introduction of annotation standards such as ToBI. Since prosody is intricately bound to the semantics of the utterance, recognition of prosodic events is important for spoken language applications such as automatic understanding and translation of speech. Moreover, corpora labeled with prosodic markers are essential for building speech synthesizers that use data-driven approaches to generate natural speech. In this paper, we build a prosody recognition system that detects stress and prosodic boundaries at the word and syllable level in American English using a coupled hidden Markov model (CHMM) to model multiple, asynchronous acoustic feature streams and a syntactic-prosodic model that captures the relationship between the syntax of the utterance and its prosodic structure. Experiments show that the recognizer achieves about 75% agreement on stress labeling and 88% agreement on boundary labeling at the syllable level.