Abstract
In this paper, speaker-independent isolated word recognition system is proposed using the Mel-Frequency Cepstral Coefficients feature extraction method to create the feature vector. Support vector machine, sigmoid neural net, and the novel wavelet neural network are used as classifiers and the results are compared in terms of the maximum accuracy obtained and the number of iterations taken to achieve this. The effect of stretch factor on the accuracy of classification for WaveNets is shown in the results. The number of features is also varied using dimension reduction technique and its effect on the accuracies is studied. The data is prepared using feature scaling and dimensionality reduction before training SVM and NN classifiers.
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Alex, J.S.R., Das, A., Kodgule, S.A., Venkatesan, N. (2018). A Comparative Study of Isolated Word Recognizer Using SVM and WaveNet. In: Nandi, A., Sujatha, N., Menaka, R., Alex, J. (eds) Computational Signal Processing and Analysis. Lecture Notes in Electrical Engineering, vol 490. Springer, Singapore. https://doi.org/10.1007/978-981-10-8354-9_13
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DOI: https://doi.org/10.1007/978-981-10-8354-9_13
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