Abstract
Various models have been proposed with many dimension reduction techniques and classifiers in the field of pattern recognition by using audio signal processing. In this paper, an effective model has been proposed for pattern recognition using PCA as the sole dimension reduction technique and Feed forward Neural network as the classifier. Twenty-eight Parkinson’s disease affected patients’ audio recordings consisting of the pronunciation of the vowels ‘A’ and ‘O’ have been used as the dataset. From these audio recordings twenty features were extracted and PCA was run on those features. PCA rearranged the feature vector matrix in a more optimized manner. Thus the optimal features were arranged in order of their significance. From this rearranged and optimized feature vector matrix, the first eight optimal features were chosen which were later used to train and test the classifier Feed forward Neural network. Experimental results demonstrate that the model can predict the occurrence and pattern of the vowels ‘A’ and ‘O’ from the audio files with very high accuracy compared to the swarm search for feature selection in classification.
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Momo, N., Abdullah, Uddin, J. (2018). Speech Recognition Using Feed Forward Neural Network and Principle Component Analysis. In: Thampi, S., Krishnan, S., Corchado Rodriguez, J., Das, S., Wozniak, M., Al-Jumeily, D. (eds) Advances in Signal Processing and Intelligent Recognition Systems. SIRS 2017. Advances in Intelligent Systems and Computing, vol 678. Springer, Cham. https://doi.org/10.1007/978-3-319-67934-1_20
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