A Comparative Study of Isolated Word Recognizer Using SVM and WaveNet

  • John Sahaya Rani Alex
  • Arka Das
  • Suhit Atul Kodgule
  • Nithya Venkatesan
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 490)


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.


Isolated word recogniser Mel-frequency cepstral coefficients Support vector machine Artificial neural network WaveNet 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • John Sahaya Rani Alex
    • 1
  • Arka Das
    • 2
  • Suhit Atul Kodgule
    • 2
  • Nithya Venkatesan
    • 2
  1. 1.School of Electronics EngineeringVIT UniversityChennaiIndia
  2. 2.School of Electrical EngineeringVIT UniversityChennaiIndia

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