The 54 th Annual Meeting of the Association for Computational Linguistics

Abstract

This paper conceptualizes speech prosody data mining and its potential application in data-driven phonology/phonetics research. We first conceptualize Speech Prosody Mining (SPM) in a time-series data mining framework. Specifically, we propose using efficient symbolic representations for speech prosody time-series similarity computation. We experiment with both symbolic and numeric representations and distance measures in a series of time-series classification and clustering experiments on a dataset of Mandarin tones. Evaluation results show that symbolic representation performs comparably with other representations at a reduced cost, which enables us to efficiently mine large speech prosody corpora while opening up to possibilities of using a wide range of algorithms that require discrete valued data. We discuss the potential of SPM using time-series mining techniques in future works.

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Cite this paper

@inproceedings{Cavar2016The5T, title={The 54 th Annual Meeting of the Association for Computational Linguistics}, author={Damir Cavar and Ewan Dunbar and Ryan Cotterell and Christo Kirov and John Sylak-Glassman and David Yarowsky and Jason Eisner}, year={2016} }