Engineering Commons^™

Automatic Speech Recognition Using Lp-Dctc/Dcs Analysis Followed By Morphological Filtering, Penny Hix

Electrical & Computer Engineering Theses & Dissertations

Front-end feature extraction techniques have long been a critical component in Automatic Speech Recognition (ASR). Nonlinear filtering techniques are becoming increasingly important in this application, and are often better than linear filters at removing noise without distorting speech features. However, design and analysis of nonlinear filters are more difficult than for linear filters. Mathematical morphology, which creates filters based on shape and size characteristics, is a design structure for nonlinear filters. These filters are limited to minimum and maximum operations that introduce a deterministic bias into filtered signals.

This work develops filtering structures based on a mathematical morphology that utilizes …

Signal Modeling With Non-Uniform Time Sampling Of Features For Automatic Speech Recognition, Montri Karnjanadecha

Electrical & Computer Engineering Theses & Dissertations

This dissertation presents an investigation of non-uniform time sampling methods for spectral/temporal feature extraction in speech. Frame-based features were computed based on an encoding of the global spectral shape using a Discrete Cosine Transform. In most current “standard” methods, trajectory (dynamic) features are determined from frame-based parameters using a fixed time sampling, i.e., fixed block length and fixed block spacing. In this research, new methods are proposed and investigated in which block length and/or block spacing are variable. The idea was initially tested with HMM-based isolated word recognition, and a significant performance improvement resulted when a variable block length and …

A Comparative Study Of Spectral Peaks Versus Global Spectral Shape As Invariant Acoustic Cues For Vowels, Amir Jalali Jagharghi

Electrical & Computer Engineering Theses & Dissertations

The primary objective of this study was to compare two sets of vowel spectral features, formants and global spectral shape parameters, as invariant acoustic cues to vowel identity. Both automatic vowel recognition experiments and perceptual experiments were performed to evaluate these two feature sets. First, these features were compared using the static spectrum sampled in the middle of each steady-state vowel versus features based on dynamic spectra. Second, the role of dynamic and contextual information was investigated in terms of improvements in automatic vowel classification rates. Third, several speaker normalizing methods were examined for each of the feature sets. Finally, …