The Application of Probabilistic Neural Network in Speech Recognition Based on Partition Clustering

Abstract:

A probabilistic neural network (PNN) speech recognition model based on the partition clustering algorithm is proposed in this paper. The most important advantage of PNN is that training is easy and instantaneous. Therefore, PNN is capable of dealing with real time speech recognition. Besides, in order to increase the performance of PNN, the selection of data set is one of the most important issues. In this paper, using the partition clustering algorithm to select data is proposed. The proposed model is tested on two data sets from the field of spoken Arabic numbers, with promising results. The performance of the proposed model is compared to single back propagation neural network and integrated back propagation neural network. The final comparison result shows that the proposed model performs better than the other two neural networks, and has an accuracy rate of 92.41%.

• Periodical: Applied Mechanics and Materials (Volumes 263-266)
• Pages: 2173-2178
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.263-266.2173
• Online since: December 2012
• Authors: Xin Guang Li, Min Feng Yao, Li Rui Jian, Zhen Jiang Li
• Keywords: Partition Clustering Algorithm, Probabilistic Neural Network (PNN), Speech Recognition

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