Date of Award

12-1992

Document Type

Thesis

Degree Name

Master of Science in Electrical Engineering

Department

Department of Electrical and Computer Engineering

First Advisor

Steven K. Rogers, PhD

Abstract

The TIMIT and KING databases, as well as a ten day AFIT speaker corpus, are used to compare proven spectral processing techniques to an auditory neural representation for speaker identification. The feature sets compared were Linear Predictive Coding (LPC) cepstral coefficients and auditory nerve firing rates using the Payton model. This auditory model provides for the mechanisms found in the human middle and inner auditory periphery as well as neural transduction. Clustering algorithms were used to generate speaker specific codebooks - one statistically based and the other a neural approach. These algorithms are the Linde-Buzo-Gray (LBG) algorithm and a Kohonen self-organizing feature map (SOFM). The LBG algorithm consistently provided optimal codebook designs with corresponding better classification rates. The resulting Vector Quantized (VQ) distortion based classification indicates the auditory model provides slightly reduced recognition in clean studio quality recordings (LPC 100%, Payton 90%), yet achieves similar performance to the LPC cepstral representation in both degraded environments (both 95%) and in test data recorded over multiple sessions (both over 98%). A variety of normalization techniques, preprocessing procedures and classifier fusion methods were examined on this biologically motivated feature set.

AFIT Designator

AFIT-GE-ENG-92D-11

DTIC Accession Number

ADA259076

Comments

The author's Vita page is omitted.

Download

Share

COinS