Theses and Dissertations
Issuing Body
Mississippi State University
Advisor
Picone, Joseph
Committee Member
Lazarou, Georgios
Committee Member
Baca, Julie
Date of Degree
8-9-2008
Document Type
Graduate Thesis - Open Access
Major
Computer Engineering
Degree Name
Master of Science
College
James Worth Bagley College of Engineering
Department
Department of Electrical and Computer Engineering
Abstract
In this work, nonlinear acoustic information is combined with traditional linear acoustic information in order to produce a noise-robust set of features for speech recognition. Classical acoustic modeling techniques for speech recognition have relied on a standard assumption of linear acoustics where signal processing is primarily performed in the signal's frequency domain. While these conventional techniques have demonstrated good performance under controlled conditions, the performance of these systems suffers significant degradations when the acoustic data is contaminated with previously unseen noise. The objective of this thesis was to determine whether nonlinear dynamic invariants are able to boost speech recognition performance when combined with traditional acoustic features. Several sets of experiments are used to evaluate both clean and noisy speech data. The invariants resulted in a maximum relative increase of 11.1% for the clean evaluation set. However, an average relative decrease of 7.6% was observed for the noise-contaminated evaluation sets. The fact that recognition performance decreased with the use of dynamic invariants suggests that additional research is required for robust filtering of phase spaces constructed from noisy time series.
URI
https://hdl.handle.net/11668/19370
Recommended Citation
May, Daniel Olen, "Nonlinear Dynamic Invariants for Continuous Speech Recognition" (2008). Theses and Dissertations. 3304.
https://scholarsjunction.msstate.edu/td/3304
Comments
dynamic systems||speech recognition