Syntactic surprisal affects spoken word duration in conversational contexts

Authors:
Vera Demberg;Asad B. Sayeed;Philip J. Gorinski;Nikolaos Engonopoulos
Affiliations:
Saarland University, Saarbrücken, Germany;Saarland University, Saarbrücken, Germany;Saarland University, Saarbrücken, Germany;Saarland University, Saarbrücken, Germany
Venue:
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Year:
2012

Citing 9
Cited 0

Cognitive factors in the evaluation of synthetic speech

Speech Communication
An efficient context-free parsing algorithm

Communications of the ACM
Robust probabilistic predictive syntactic processing: motivations, models, and applications

Robust probabilistic predictive syntactic processing: motivations, models, and applications
Probabilistic top-down parsing and language modeling

Computational Linguistics
A probabilistic earley parser as a psycholinguistic model

NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Deterministic left corner parsing

SWAT '70 Proceedings of the 11th Annual Symposium on Switching and Automata Theory (swat 1970)
Between linguistic attention and gaze fixations inmultimodal conversational interfaces

Proceedings of the 2009 international conference on Multimodal interfaces
Deriving lexical and syntactic expectation-based measures for psycholinguistic modeling via incremental top-down parsing

EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Uncertainty reduction as a measure of cognitive processing effort

CMCL '10 Proceedings of the 2010 Workshop on Cognitive Modeling and Computational Linguistics

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present results of a novel experiment to investigate speech production in conversational data that links speech rate to information density. We provide the first evidence for an association between syntactic surprisal and word duration in recorded speech. Using the AMI corpus which contains transcriptions of focus group meetings with precise word durations, we show that word durations correlate with syntactic surprisal estimated from the incremental Roark parser over and above simpler measures, such as word duration estimated from a state-of-the-art text-to-speech system and word frequencies, and that the syntactic surprisal estimates are better predictors of word durations than a simpler version of surprisal based on trigram probabilities. This result supports the uniform information density (UID) hypothesis and points a way to more realistic artificial speech generation.