Title :
Genetically optimised feedforward neural networks for speaker identification
Author :
Price, R.C. ; Willmore, J.P. ; Roberts, W.J.J. ; Zyga, K.J.
Author_Institution :
Div. of Inf. Technol., Defence Sci. & Technol. Organ., Salisbury, SA, Australia
Abstract :
The problem of identifying a speaker from a given utterance has been conventionally addressed using techniques such as Gaussian mixture models (GMMs) that model the characteristics of a known speaker via means and covariances. We compare the performance of a genetically optimised neural network speaker identification system versus the conventional approach of GMMs. The test data used in the experiments was the data used for the 1996 National Institute for Standards Technology (NIST) evaluation of speaker identification systems
Keywords :
feedforward neural nets; genetic algorithms; speaker recognition; Gaussian mixture models; covariances; genetically optimised feedforward neural networks; means; speaker identification; utterance; Cost function; Feedforward neural networks; Genetic algorithms; Microphones; NIST; Network topology; Neural networks; Speech; Testing; Training data;
Conference_Titel :
Knowledge-Based Intelligent Engineering Systems and Allied Technologies, 2000. Proceedings. Fourth International Conference on
Conference_Location :
Brighton
Print_ISBN :
0-7803-6400-7
DOI :
10.1109/KES.2000.884093
