Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/3441
Title: Feature extraction and dimensionality reduction in pattern recognition with applications in speech recognition
Authors: Jiang, Hai
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
DRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognition
Issue Date: 2006
Source: Jiang, H. (2006). Feature extraction and dimensionality reduction in pattern recognition with applications in speech recognition. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: Speech recognition has become a challenging task to create an intelligent recognizer that emulates a human being’s ability in speech perception under all environments. The feature extraction of speech is one of the most import issues in the field of speech recognition. In order to achieve high recognition accuracy, the feature extractor is required to discover salient characteristics suited for classification. In this thesis, feature extraction methods and dimensionality reduction methods for feature space are examined. This thesis is divided in three parts. In the first part, speech recognition techniques are reviewed, and several linear and non-linear dimensionality reduction methods are investigated. In the second part, a new linear and a non-linear dimensionality reduction method are proposed in this thesis. In the last part, a new feature extraction technique for speech recognition is presented.
URI: https://hdl.handle.net/10356/3441
DOI: 10.32657/10356/3441
Rights: Nanyang Technological University
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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