Nonlinear Discriminant Analysis Based Feature Dimensionality Reduction for Automatic Speech Recognition

Аннотация

Automatic Speech Recognition (ASR) has advanced to the point where state of the art speech recognition algorithms perform reasonably well even for large vocabulary continuous speech recognition in practical environments. Among speech recognition problems, feature extraction, which compresses a speech signal into streams of acoustical feature vectors, has become even more important for ASR since acoustical modeling methods have been well established and language modeling largely depends on the nature of the targeted language. The focus of this dissertation is the determination of effective speech features, where both spectral and temporal variations in speech are captured in a low dimensional representation, for speech recognition tasks.