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ICASSP
2009
IEEE

Voronoi cell shaping for feature selection with discrete HMMs

10 years 4 months ago
Voronoi cell shaping for feature selection with discrete HMMs
In this paper, we introduce a novel vector quantization (VQ) scheme for distributing the quantization error equally among the quantized dimensions. Afterwards, the proposed VQ scheme is used to perform feature selection in on-line handwritten whiteboard note recognition based on discrete Hidden-Markov-Models (HMMs). In an experimental section we show that the novel VQ scheme derives feature sets which contain less than 50 % features, enabling recognition with better performance at less computational costs. Finally, the derived feature set is compared to the quantized features selected within a continuous HMM-based system: the features selected after quantization with the proposed VQ scheme are proved to perform significantly better than those in the continuous system.
Joachim Schenk, Gerhard Rigoll
Added 21 May 2010
Updated 21 May 2010
Type Conference
Year 2009
Where ICASSP
Authors Joachim Schenk, Gerhard Rigoll
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