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.
Recommended Citation
V. Moonasar and G. K. Venayagamoorthy, "Speaker Identification Using a Combination of Different Parameters as Feature Inputs to an Artificial Neural Network Classifier," Proceedings of IEEE Africon, 1999, Institute of Electrical and Electronics Engineers (IEEE), Jan 1999.
The definitive version is available at https://doi.org/10.1109/AFRCON.1999.820791
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.
Publication Date
01 Jan 1999
