Automatic prosodic prominence detection in speech using acoustic features: an unsupervised system

This paper presents work in progress on the automatic detection of prosodic prominence in continuous speech. Prosodic prominence involves two different phonetic features: pitch accents, connected with fundamental frequency (F0) movements and syllable overall energy, and stress, which exhibits a strong correlation with syllable nuclei duration and mid-to-high-frequency emphasis. By measuring these acoustic parameters it is possible to build an automatic system capable of correctly identifying prominent syllables with an agreement, with human-tagged data, comparable with the inter-human agreement reported in the literature. This system does not require any training phase, additional information or annotation, it is not tailored to a specific set of data and can be easily adapted to different languages.

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