Linear discriminant analysis for speechreading

G. Potamianos, H. Graf
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引用次数: 35

Abstract

This paper investigates the use of Fisher-Rao (1965) linear discriminant analysis (LDA) as a means of visual feature extraction for hidden Markov model based automatic speechreading. For every video frame, a three-dimensional region of interest containing the speaker's mouth over a sequence of adjacent frames is lexicographically arranged into a data vector. Such vectors are then projected onto the space of the most discriminant "eigensequences", estimated by means of LDA on a training set of image sequence vectors, labeled from a set of a-priori chosen classes. The resulting projections, as well as their first and second derivatives over time, are used as features for automatic speechreading. The proposed method is applied to single-speaker, multi-speaker, and speaker-independent visual-only recognition tasks, consistently outperforming principal component analysis and discrete wavelet transform based visual features. Specific issues relevant to LDA are also discussed, namely, class selection, automatic data class labelling, and dimensionality reduction prior to LDA.
语音阅读的线性判别分析
本文研究了Fisher-Rao(1965)线性判别分析(LDA)作为一种基于隐马尔可夫模型的自动语音朗读的视觉特征提取方法。对于每一个视频帧,一个三维感兴趣的区域包含说话人的嘴在一系列相邻的帧被字典排列成一个数据向量。然后将这些向量投影到最具判别性的“特征序列”空间中,通过LDA对图像序列向量的训练集进行估计,并从一组先验选择的类中标记。由此产生的投影,以及它们随时间的一阶和二阶导数,被用作自动语音读取的特征。该方法适用于单扬声器、多扬声器和独立扬声器的视觉识别任务,始终优于基于主成分分析和离散小波变换的视觉特征。还讨论了与LDA相关的具体问题,即类选择、自动数据类标记和LDA之前的降维。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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