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首页> 外文会议>International Conference on Computer Analysis of Images and Patterns(CAIP 2007); 20070827-29; Vienna(AT) >A New Manifold Representation for Visual Speech Recognition
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A New Manifold Representation for Visual Speech Recognition

机译：视觉语音识别的新流形表示

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摘要

In this paper, we propose a new manifold representation capable of being applied for visual speech recognition. In this regard, the real time input video data is compressed using Principal Component Analysis (PCA) and the low-dimensional points calculated for each frame define the manifolds. Since the number of frames that from the video sequence is dependent on the word complexity, in order to use these manifolds for visual speech classification it is required to re-sample them into a fixed number of keypoints that are used as input for classification. In this paper two classification schemes, namely the k Nearest Neighbour (kNN) algorithm that is used in conjunction with the two-stage PCA and Hidden-Markov-Model (HMM) classifier are evaluated. The classification results for a group of English words indicate that the proposed approach is able to produce accurate classification results.
机译：在本文中，我们提出了一种新的流形表示形式，可以应用于视觉语音识别。在这方面，实时输入视频数据使用主成分分析（PCA）进行压缩，并且为每个帧计算的低维点定义了歧管。由于来自视频序列的帧数取决于单词的复杂程度，因此为了将这些流形用于视觉语音分类，需要将它们重新采样为固定数量的关键点，这些关键点用作分类的输入。本文评估了两种分类方案，即与两级PCA和隐马尔可夫模型（HMM）分类器结合使用的k最近邻（kNN）算法。一组英语单词的分类结果表明，该方法能够产生准确的分类结果。

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