10th Annual Conference of the International Speech Communication Association

Brighton, United Kingdom
September 6-10, 2009

Self-Learning Vector Quantization for Pattern Discovery from Speech

Okko Johannes Räsänen, Unto Kalervo Laine, Toomas Altosaar

Helsinki University of Technology, Finland

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.

Full Paper

Bibliographic reference.  Räsänen, Okko Johannes / Laine, Unto Kalervo / Altosaar, Toomas (2009): "Self-learning vector quantization for pattern discovery from speech", In INTERSPEECH-2009, 852-855.