Title: Enhanced recognition rate of spoken Hindi paired word using probabilistic neural network approach
Authors: Dinesh Kumar Rajoriya, R.S. Anand, R.P. Maheshwari
Addresses: Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee-247667, Uttarakhand, India. ' Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee-247667, Uttarakhand, India. ' Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee-247667, Uttarakhand, India
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.
Keywords: probabilistic neural networks; PNNs; wavelet transform; correct recognition rate; CRR; spoken Hindi; paired words; speaker dependent systems; speaker independent systems; classification; pattern recognition; spoken word patterns; word recognition.
International Journal of Information and Communication Technology, 2011 Vol.3 No.2, pp.148 - 159
Published online: 02 Aug 2011 *Full-text access for editors Access for subscribers Purchase this article Comment on this article