Theses and Dissertations

Issuing Body

Mississippi State University

Advisor

Picone, Joseph

Committee Member

Lazarou, Georgios

Committee Member

Baca, Julie

Date of Degree

1-1-2008

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

Comments

dynamic systems||speech recognition

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