Optimal Models of Prosodic Prominence
نویسندگان
چکیده
منابع مشابه
Unsupervised prominence prediction for speech synthesis
We propose an unsupervised prominence prediction method for expressive speech synthesis. Prominence patterns are learned by statistical analysis of prosodic features extracted from speech data. The advantages of our unsupervised datadriven prominence prediction include: easy adaptation to new speakers, speech styles, and even languages without requiring expert knowledge or complicated linguisti...
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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 w...
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The perception of prosodic prominence is influenced by different sources like different acoustic cues, linguistic expectations and context. We use a generalized additive model and a random forest to model the perceived prominence on a corpus of spoken German. Both models are able to explain over 80% of the variance. While the random forests give us some insights on the relative importance of th...
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تاریخ انتشار 2011