A prominence model is proposed for enhancing prosodic features in automatic lexical stress and pitch accent detection. We make use of a loudness model and incorporate differential pitch values to improve conventional features. Experiments show that these new prosodic features can improve the detection of lexical stress and pitch accent by about 6%. We further employ a prominence model to take into account of effects from neighboring syllables. For pitch accent detection, we achieve a further performance improvement from 80.61% to 83.30%. For lexical stress detection, we achieve performance improvements in (i) classification of primary, secondary and unstressed syllables (from 76.92% to 78.64%), as well as (ii) determining the presence or absence of primary stress (from 86.99% to 89.80%).