When discrete Hidden-Markov-Models (HMMs)-based recognition is performed, vector quantization (VQ) is used to transform continuous observations to sequences of discrete symbols. After VQ, the quantization error is not spread equally among the features. This impairs the feature significance, which is important when features are selected, e. g. by applying the Sequential Forward Selection (SFS). In this paper, we introduce a novel vector quantization (VQ) scheme for distributing the quantization error equally among the quantized dimensions of a feature vector. Afterwards, the proposed VQ scheme is used to apply the SFS on the features in on-line handwritten whiteboard note recognition based on discrete HMMs. In an experimental section, we show that the novel VQ scheme derives feature sets of almost half the size of the feature sets gained when standard VQ is used for quantization, while the performance stays the same.
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机译：当执行离散的隐藏马克型 - 基础（HMMS）识别时，矢量量化（VQ）用于将连续观察转换为离散符号的序列。在VQ之后，量化误差不会在特征之间同等地扩展。 This impairs the feature significance, which is important when features are selected, e. G。通过应用顺序前进选择（SFS）。在本文中，我们介绍了一种新颖的矢量量化（VQ）方案，用于在特征向量的量化尺寸之间同样地分布量化误差。之后，建议的VQ方案用于基于离散HMMS在线手写的白板笔记识别中的特征应用SFS。在一个实验部分中，我们表明，当使用标准VQ用于量化时，新颖的VQ方案衍生几乎一半的特征集大小的特征集，而性能保持不变。
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