2016 INTERSPEECH INTERSPEECH 2016

Analyzing the Contribution of Top-Down Lexical and Bottom-Up Acoustic Cues in the Detection of Sentence Prominence

Abstract

Recent work has suggested that prominence perception could be driven by the predictability of the acoustic prosodic features of speech. On the other hand, lexical predictability and part of speech information are also known to correlate with prominence. In this paper, we investigate how the bottom-up acoustic and top-down lexical cues contribute to sentence prominence by using both types of features in unsupervised and supervised systems for automatic prominence detection. The study is conducted using a corpus of Dutch continuous speech with manually annotated prominence labels. Our results show that unpredictability of speech patterns is a consistent and important cue for prominence at both the lexical and acoustic levels, and also that lexical predictability and part-of-speech information can be used as efficient features in supervised prominence classifiers.

πŸš€ Conference Pioneer β€” INTERSPEECH 2016
πŸŒ‰ Interdisciplinary Bridge β€” Machine Learning and Mathematics & Optimization
🧭 Keyword Pioneer β€” lexical cue
🐝 Cross-Pollinator β€” Artificial Intelligence, Computer Science, Deep Learning, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Speech & Audio