LDR-LLE: LLE with Low-Dimensional Neighborhood Representation
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
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This paper describes a novel speech visualization method that creates a readable pattern based on locally linear embedding (LLE). LLE is an unsupervised learning algorithm for feature extraction. If the speech variability is described by a small number of continuous features, then we can imagine the data as lying on a low dimensional manifold in the high dimensional space of speech waveforms. The goal of feature extraction is to reduce the dimensionality of the speech signal while preserving the informative signatures. Firstly, speech signal undergoes a series of preprocessing course. Secondly, we make use of LLE decreasing the dimension of speech feature vector. Finally, we utilize plot display algorithm to generate a speech plot. The algorithm consists of three parts: coordinates partition, area computation and coordinates calculation of speech feature value. The speech feature after dimension decreasing by LLE is displayed on the CRT by plot patterns and the deaf can utilize their own brain to identify different speech for training their oral ability effectively.