Title
Competitive radial basis functions training for phone classification
Abstract
In this paper we describe the design of a phoneme classifier that is based on AIDA, a speech database that has been recently proposed as a standard for Italian concerning the phonetic level. We present experimental results using LVQ and show that the proper selection of Kohonen's learning parameter α, based on some intriguing links with Backpropagation learning, contributes to improve the performance with respect to standard heuristics proposed in the literature [Konen, Proc. IEEE 78 (9) (1990) 1464–1480].
Year
DOI
Venue
2000
10.1016/S0925-2312(00)00312-X
Neurocomputing
Keywords
Field
DocType
Automatic speech recognition,Backpropagation,Competitive radial basis functions,Learning vector quantization,Phone classification,Radial basis functions
Radial basis function network,Radial basis function,Computer science,Learning vector quantization,Self-organizing map,Heuristics,Phone,Artificial intelligence,Backpropagation,Machine learning
Journal
Volume
Issue
ISSN
34
1-4
0925-2312
Citations 
PageRank 
References 
4
0.43
14
Authors
5
Name
Order
Citations
PageRank
Piero Cosi122443.27
Paolo Frasconi22984368.70
Marco Gori383983.06
luca lastrucci440.43
Giovanni Soda5957174.35