A novel technique for speaker independent automated speech recognition is proposed. We take a segment model approach to Automated Speech Recognition (ASR), considering the trajectory of an utterance in vector space, then classify using a modified Probabilistic Neural Network (PNN) and maximum likelihood rule. The system performs favourably with established techniques. Our system achieves in excess of 94% with isolated digit recognition, 88% with isolated alphabetic letters, and 83% with the confusable /e/ set. A favourable compromise between recognition accuracy and computer memory and speech can also be reached by performing clustering on the training data for the PNN.
Cite as: Low, R., Togneri, R. (1998) Speech recognition using the probabilistic neural network. Proc. 5th International Conference on Spoken Language Processing (ICSLP 1998), paper 0645, doi: 10.21437/ICSLP.1998-840
@inproceedings{low98b_icslp,
author={Raymond Low and Roberto Togneri},
title={{Speech recognition using the probabilistic neural network}},
year=1998,
booktitle={Proc. 5th International Conference on Spoken Language Processing (ICSLP 1998)},
pages={paper 0645},
doi={10.21437/ICSLP.1998-840}
}