This paper presents an approach to estimating word level prominence in Swedish using syllable level features. The paper discusses the mismatch problem of annotations between word level perceptual prominence and its acoustic correlates, context, and data scarcity. 200 sentences are annotated by 4 speech experts with prominence on 3 levels. A linear model for feature extraction is proposed on a syllable level features, and weights of these features are optimized to match word level annotations. We show that using syllable level features and estimating weights for the acoustic correlates to minimize the word level estimation error gives better detection accuracy compared to word level features, and that both features exceed the baseline accuracy.
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