Abstract

This paper presents a technique using artificial neural networks (ANNs) for speaker identification that results in a better success rate compared to other techniques. The technique used in this paper uses both power spectral densities (PSDs) and linear prediction coefficients (LPCs) as feature inputs to a self organizing feature map to achieve a better identification performance. Results for speaker identification with different methods are presented and compared.

Meeting Name

IEEE Africon, 1999

Department(s)

Electrical and Computer Engineering

Keywords and Phrases

ANN; Artificial Neural Network Classifier; Feature Inputs; Identification Performance; Linear Prediction Coefficients; Pattern Classification; Power Spectral Densities; Prediction Theory; Self Organizing Feature Map; Self-Organising Feature Maps; Speaker Identification; Speaker Recognition; Spectral Analysis; Success Rate

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 1999 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

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