We propose a novel universal acoustic characterization approach to spoken language identification (LID), in which any spoken language is described with a common set of fundamental units defined universally. Specifically, manner and place of articulation form this unit inventory and are used to build a set of universal attribute models with data-driven techniques. Using the vector space modeling approaches to LID a spoken utterance is first decoded into a sequence of attributes. Then, a feature vector consisting of co-occurrence statistics of attribute units is created, and the final LID decision is implemented with a set of vector space language classifiers. Although the present study is just in its preliminary stage, promising results comparable to acoustically rich phone-based LID systems have already been obtained on the NIST 2003 LID task. The results provide clear insight for further performance improvements and encourage a continuing exploration of the proposed framework.
Cite as: Siniscalchi, S.M., Reed, J., Svendsen, T., Lee, C.-H. (2009) Exploring universal attribute characterization of spoken languages for spoken language recognition. Proc. Interspeech 2009, 168-171, doi: 10.21437/Interspeech.2009-67
@inproceedings{siniscalchi09_interspeech,
author={Sabato Marco Siniscalchi and Jeremy Reed and Torbjørn Svendsen and Chin-Hui Lee},
title={{Exploring universal attribute characterization of spoken languages for spoken language recognition}},
year=2009,
booktitle={Proc. Interspeech 2009},
pages={168--171},
doi={10.21437/Interspeech.2009-67}
}