3PRO – An unsupervised method for the automatic detection of sentence prominence in speech

Sofoklis Kakouros*, Okko Räsänen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

16 Citations (Scopus)


Automatic detection of prominence in speech has attracted interest in recent years due to its multiple uses in spoken language applications. However, typical approaches require manual labeling of the data that is an expensive and time consuming process, also making the systems potentially specific to the language or speaking style in question. In this paper, we propose a novel unsupervised algorithm for the automatic detection of sentence prominence named 3PRO (Prominence from Prosodic Probabilities; “three-pro”) that is based on recent findings on human perception of prominence in speech. By combining syllable duration information to the level of surprisal observed in the acoustic prosodic features, the method is capable of estimating prominent words from continuous speech without labeled training data. The algorithm is evaluated by comparing model output to manually transcribed prominence labels on a Dutch and French speech corpus, showing performance levels close to supervised prominence classifiers operating on the same data.

Original languageEnglish
Pages (from-to)67-84
Number of pages18
JournalSpeech Communication
Publication statusPublished - 1 Sep 2016
MoE publication typeA1 Journal article-refereed


  • Attention
  • Prominence detection
  • Prominence perception
  • Speech analysis
  • Speech processing
  • Stimulus predictability

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