Acoustic Modeling and Feature Selection for Speech Recognition
Zheng, Yanli
This item is only available for download by members of the University of Illinois community. Students, faculty, and staff at the U of I may log in with your NetID and password to view the item. If you are trying to access an Illinois-restricted dissertation or thesis, you can request a copy through your library's Inter-Library Loan office or purchase a copy directly from ProQuest.
Permalink
https://hdl.handle.net/2142/80914
Description
Title
Acoustic Modeling and Feature Selection for Speech Recognition
Author(s)
Zheng, Yanli
Issue Date
2005
Doctoral Committee Chair(s)
Mark Hasegawa-Johnson
Department of Study
Electrical Engineering
Discipline
Electrical Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Engineering, Electronics and Electrical
Language
eng
Abstract
The investigation of the thesis can be divided into three parts. In the first part, a nonlinear dynamic system is proposed for formant tracking. Compared to previous formant trackers depending on least squares estimation of LPC coefficients, MUSIC (Multiple Signal Classification) and ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques) are used to improve the accuracy of formant estimation. Furthermore, a mixture of nonlinear dynamic systems is developed to improve the performance of formant tracking. In the second part, the formant tracker system is extended to perform phoneme recognition. The results indicate that the incapability of estimating the system measurement error prevents the system from performing well in the phoneme recognition tasks. In the third part, an SVM and HMM combined system is used to prove that the formant information is indeed useful to distinguish different phonemes. And the result in this part suggests that the output of the SVM can be treated as a particular case of discriminant transformation of the original acoustic space and might be useful for speech recognition.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
