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Type of Document	Master's Thesis
Author	wang, meng ,
Author's Email Address	mwang3@unity.ncsu.edu
URN	etd-09252003-161752
Title	Development of Digital Signal Processing and Statistical Classification Methods for Distinguishing Nasal Consonants
Degree	Master of Science
Graduate Program	Operations Research
Advisory Committee	Advisor Name Title David Mcallister Committee Chair Donald Bitzer Committee Member Zhang,Hao Committee Member
Keywords	Digital signal processing statistics classification nasal consonants
Date of Defense	2003-08-19
Availability	unrestricted
Abstract Abstract WANG, MENG. Development of Digital Signal Processing and Statistical Classification Methods for Distinguishing Nasal Consonants. (Under the direction of David McAllister.) For almost half a century, people have been looking for efficient classifiers to distinguish two nasal sounds, / / from / /, uttered by a single speaker. From the middle of the last decade, there has been little progress in research on this topic. In recent years, we, researchers of the Voice I/O Group in Department of Computer Science at North Carolina State University, have conducted some new trials on this classical problem. In this thesis, those trials are briefly summarized. Instead of simply using the Fourier transform to produce the spectra as people usually did in the past, the author uses other kinds of transforms to extract more feature differences between / / and / /. The new transforms can be the alternatives of frequencies, such as singular values or eigenvalues, or even other transforms such as wavelets, which can deal with non-stationary systems quite well. We combine together the old and new features to get a larger feature vector, which will bring more classification information. We collect multiple voice samples of a single speaker and calculate the above feature representations, then use them as input of some popular statistical classification techniques, such as Principle Component Analysis (PCA), Discriminant Analysis (DA), and Support Vector Machine (SVM). By way of one training process, one testing process, and one heuristic scheme, we can identify the nasals with low error rates.
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