Title :
Temporal sequence classification by memory neuron networks
Author :
Poddar, Pinaki ; Rao, P.V.S.
Author_Institution :
Comput. Syst. & Commun. Group, Tata Inst. of Fundamental Res., Bombay, India
Abstract :
A recurrent connectionist architecture, called memory neuron network (MNN), is applied for classification of temporal sequences. The network architecture allows a learnable parametric representation of the activation history of the units. It has been shown that the network is generalized version of network with time-delays. The learning protocol has been developed to train a collection of such networks as discriminant models for classes of temoral sequences. The design is tested in classification of voiced plosives /B/,/D/,/G/. Due to continuous movement of the articulators, the spectral characteristics of the speech signal change during transitions from one phoneme to the other. MNN has been used to model this dynamic behavior during the transitions from plosive sounds to vowels
Keywords :
pattern classification; recurrent neural nets; spectral analysis; speech recognition; activation history; continuous articulator movement; discriminant models; learnable parametric representation; learning protocol; memory neuron networks; phoneme transitions; recurrent connectionist architecture; spectral characteristics; temporal sequence classification; voiced plosives; vowels; Computer architecture; Computer networks; Delay; History; Kernel; Multi-layer neural network; Neurons; Pattern classification; Speech; Stochastic processes;
Conference_Titel :
Neural Networks for Processing [1993] III. Proceedings of the 1993 IEEE-SP Workshop
Conference_Location :
Linthicum Heights, MD
Print_ISBN :
0-7803-0928-6
DOI :
10.1109/NNSP.1993.471871
