A novel and computationally straightforward clustering algorithm was developed for vector quantization (VQ) of speech signals for a task of unsupervised pattern discovery (PD) from speech. The algorithm works in purely incremental mode, is computationally extremely feasible, and achieves comparable classification quality with the well-known k-means algorithm in the PD task. In addition to presenting the algorithm, general findings regarding the relationship between the amounts of training material, convergence of the clustering algorithm, and the ultimate quality of VQ codebooks are discussed.
Cite as: Räsänen, O.J., Laine, U.K., Altosaar, T. (2009) Self-learning vector quantization for pattern discovery from speech. Proc. Interspeech 2009, 852-855, doi: 10.21437/Interspeech.2009-259
@inproceedings{rasanen09_interspeech,
author={Okko Johannes Räsänen and Unto Kalervo Laine and Toomas Altosaar},
title={{Self-learning vector quantization for pattern discovery from speech}},
year=2009,
booktitle={Proc. Interspeech 2009},
pages={852--855},
doi={10.21437/Interspeech.2009-259}
}