att_abstract={Automatic detection of word prominence can provide valuable information for downstream applications such as spoken language understanding. Prior work on automatic word prominence detection exploit a variety of lexical, syntactic, and prosodic features and model the task as a sequence of local classifications (independently or using history). While lexical and syntactic features are highly correlated with the notion of word prominence, the output of speech recognition is typically noisy and hence these features are less reliable than the acoustic-prosodic feature stream. In this work, we address the
automatic detection of word prominence through novel prosodic features that capture the changes in F0 curve shape and magnitude along with duration and energy. We contrast the utility of these features with aggregate statistics of F0, duration, and energy used in prior work. Our features
are simple to compute yet robust to the inherent difficulties associated with identifying salient points (such as F0 peaks, valleys, onsets, offsets, etc.) within the F0 contour. We demonstrate that these novel features are substantially more predictive than the standard aggregation-based prosodic features using feature analysis. Experimental results on a corpus of spontaneous speech indicate that the accuracy obtained using only the prosodic features is better than using both lexical and syntactic features.},
	att_authors={tm330a, vk947h, ac1234},
	att_copyright_notice={The definitive version was published in   2012. {{, 2012-09-09}}
	att_tags={Speech analysis,  automatic word prominence detection,  prosodic features.},
	author={Taniya Mishra AND Vivek kumar Rangarajan sridhar AND Alistair Conkie},
	booktitle={Proceedings of Interspeech},
	institution={{Interspeech 2012}},
	title={{Word Prominence Detection using Robust yet Simple Prosodic Features}},