Towards style transformation from written-style to audio-style

Authors:
Amjad Abu-Jbara;Barbara Rosario;Kent Lyons
Affiliations:
University of Michigan, Ann Arbor, MI;Intel Labs, Santa Clara, CA;Intel Labs, Santa Clara, CA
Venue:
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Year:
2011

Citing 8
Cited 0

Play it again: a study of the factors underlying speech browsing behavior

CHI 98 Cconference Summary on Human Factors in Computing Systems
Gene Selection for Cancer Classification using Support Vector Machines

Machine Learning
Automatic text categorization in terms of genre and author

Computational Linguistics
Extracting paraphrases from a parallel corpus

ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Stylistic text classification using functional lexical features: Research Articles

Journal of the American Society for Information Science and Technology
Automatic paraphrase acquisition from news articles

HLT '02 Proceedings of the second international conference on Human Language Technology Research
Application-driven statistical paraphrase generation

ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Generating phrasal and sentential paraphrases: A survey of data-driven methods

Computational Linguistics

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we address the problem of optimizing the style of textual content to make it more suitable to being listened to by a user as opposed to being read. We study the differences between the written style and the audio style by consulting the linguistics and journalism literatures. Guided by this study, we suggest a number of linguistic features to distinguish between the two styles. We show the correctness of our features and the impact of style transformation on the user experience through statistical analysis, a style classification task, and a user study.