Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.