Subspace-Based Feature Representation and Learning for Language Recognition

Abstract

This paper presents a novel subspace-based approach for phonotactic language recognition. The whole framework is divided into two parts: the speech feature representation and the subspacebased learning algorithm. First, the phonetic information as well as the contextual relationship, possessed by spoken utterances, are more abundantly retrieved by… (More)

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Cite this paper

@inproceedings{Shih2012SubspaceBasedFR, title={Subspace-Based Feature Representation and Learning for Language Recognition}, author={Yu-Chin Shih and Hung-Shin Lee and Hsin-Min Wang and Shyh-Kang Jeng}, booktitle={INTERSPEECH}, year={2012} }