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Analysis and Determination of Inner Lip Texture Descriptors for Visual Speech Representation

机译：视觉语音表示的内唇纹理描述符的分析与确定

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—The problem of visual speech representation for bimodal based speech recognition includes particular challenges in the modeling of the inner lip texture reflecting different pronunciations, such as the appearance of teeth and tongue. This paper proposes and analyzes several possible statistical inner lip texture descriptors to determine an effective and discriminant feature. Simply using grayscale without full specification of the underlying colour model tends to loss some significant discriminative information. Therefore thorough exploration on the color space components selection in computing the local inner lip texture is thus a primary goal of the present research. The L channel of Lab color space is finally determined as the basis for the development of the inner lip texture model. Through feature level fusion, the final classification of visual speech is performed based on the proposed inner lip texture descriptor and standard geometric features. Together with audio speech, this paper furthers the development of the CHMM based bimodal Chinese character pronunciation recognition system. The experimental results show that the local inner texture descriptors, such as the color moment with geometric feature, outperform the holistic inner texture descriptors, such as the statistical histogram, in representing visual speech with the close discriminability but low dimensionality.
机译：- 基于双模语音识别的视觉语音表示的问题包括内唇纹理建模的特定挑战，反射不同的发音，例如齿和舌头的外观。本文提出并分析了几种可能的统计内唇纹理描述符以确定有效和判别的特征。只需使用底层颜色模型的完整规范的灰度才能损失一些显着的歧视信息。因此，对计算局部内唇纹理的颜色空间分量选择的彻底探索是本研究的主要目标。 Lab颜色空间的L通道最终被确定为内唇纹理模型的开发的基础。通过特征级融合，基于所提出的内唇纹理描述符和标准几何特征来执行视觉语音的最终分类。本文一起与音频语音一起传递了基于CHMM的双峰汉字发音识别系统的发展。实验结果表明，局部内部纹理描述符，例如具有几何特征的颜色时刻，优于整体内部纹理描述符，例如统计直方图，表示具有紧密可辨认性但低维度的视觉语音。

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