Spoken language identification consists in recognizing a language based on a sample of speech from an unknown speaker. The traditional approach for this task mainly considers the phonothactic information of languages. However, for marginalized languages - languages with few speakers or oral languages without a fixed writing standard -, this information is practically not at hand and consequently the usual approach is not applicable. In this paper, we present a method that only considers the acoustic features of the speech signal and does not use any kind of linguistic information. This method applies a wavelet transform to extract the acoustic features of the speech signal. The experimental results on a pairwise discrimination task among nine languages demonstrated that this approach considerably outperforms other previous methods based on the sole use of acoustic features.
Cite as: Reyes-Herrera, A.L., Villaseñor-Pineda, L., Montes-y-Gómez, M. (2006) Automatic language identification using wavelets. Proc. Interspeech 2006, paper 1998-Mon2CaP.1, doi: 10.21437/Interspeech.2006-132
@inproceedings{reyesherrera06_interspeech,
author={Ana Lilia Reyes-Herrera and Luis Villaseñor-Pineda and Manuel Montes-y-Gómez},
title={{Automatic language identification using wavelets}},
year=2006,
booktitle={Proc. Interspeech 2006},
pages={paper 1998-Mon2CaP.1},
doi={10.21437/Interspeech.2006-132}
}