This study investigates the relationship between sentence
prominence and the predictability of word-specific statistical
descriptors of prosody. We extend from an earlier wordinvariant
model by studying a model that marks words as
prominent if the acoustic prosodic features differ from their
expected values during the lexemes. To test the approach, the
most common acoustic features associated with the perception
of prominence are extracted and several lexeme-specific
statistical measures are computed for each feature.
Simulations are conducted on a corpus of continuous English
speech and the algorithm output is compared to manually
assigned prominence labels. The results show that the deviant
prosodic descriptors of the words correlate with the
perception of prominence. However, this effect is much
smaller than that obtained by modeling the prosodic
predictability at the utterance level, suggesting that contextindependent
lexeme-specific models are unable to capture
relevant aspects of sentence prominence