Title :
A comparison of different clustering algorithms for speech recognition
Author :
Goddard, J. ; Martinez, A.E. ; Martinez, F.M. ; Aljama, T.
Author_Institution :
Depto. de Ingenieria Electrica, Univ. Autonoma Metropolitana, Mexico City, Mexico
Abstract :
K-means and SOM have been frequently applied to clustering problems in speech recognition. Recently, new clustering algorithms have been introduced which present certain advantages over both of them. The present paper compares the performance of one of these, STVQ, to k-means and SOM on two well-known speech data sets
Keywords :
pattern clustering; speech recognition; SOM algorithm; STVQ algorithm; clustering algorithms; k-means algorithm; performance comparison; speech recognition; Automatic speech recognition; Clustering algorithms; Convergence; Cost function; Genetic expression; Minimization methods; Partitioning algorithms; Prototypes; Speech recognition; Vector quantization;
Conference_Titel :
Circuits and Systems, 2000. Proceedings of the 43rd IEEE Midwest Symposium on
Conference_Location :
Lansing, MI
Print_ISBN :
0-7803-6475-9
DOI :
10.1109/MWSCAS.2000.951435
