Enhanced recognition rate of spoken Hindi paired word using probabilistic neural network approach

Abstract

Probabilistic neural network (PNN) shows efficient capability in recognising low level signal patterns. PNN is a sequential arrangement of radial basis layer and a competitive transfer function layer, which picks up the highest probabilities as a final recognition result. In the present paper, wavelet transform technique has been used for attributes extraction from spoken word patterns and PNN for classification/recognition. For experimental set up, spoken Hindi (Indian official language) paired word utterances have been collected from individuals for development of database, and evaluation purpose. The proposed approach provides enhanced correct recognition rate (CRR) for spoken Hindi paired word recognition system, with considered dataset. CRR has been found to be 71.08% for speaker independent spoken Hindi paired word recognition system.