Abstract
The research on signal processing of syllables sound signal is still the challenging tasks, due to non-stationary, speaker-dependent, variable context, and dynamic nature factor of the signal. In the process of classification using multi-layer perceptron (MLP), the process of selecting a suitable parameter of hidden neuron and hidden layer is crucial for the optimal result of classification. This paper presents a speech signal classification method by using MLP with various numbers of hidden-layer and hidden-neuron for classifying the Indonesian Consonant-Vowel (CV) syllables signal. Five feature sets were generated by using Discrete Wavelet Transform (DWT), Renyi Entropy, Autoregressive Power Spectral Density (AR-PSD) and Statistical methods. Each syllable was segmented at a certain length to form a CV unit. The results show that the average recognition of WRPSDS with 1, 2, and 3 hidden layers were 74.17%, 69.17%, and 63.03%, respectively.
Highlights
One of main goal in speech recognition is to obtain the best accuracy for recognizing or classifying the speech signal uttered by speaker
This paper presents the classification of Indonesian CV syllables sound signal by using the Multi-layer Perceptron (MLP) in varying number of hidden neuron, and the signal processing by using Discrete Wavelet Transform (DWT), Renyi entropy (RE), Autoregressive Power Spectral Density (AR-PSD), and statistical for generating features [10]
The result showed that WRPSDS has the highest score in average accuracy, but at ninth hidden neuron, the score of WS is higher than WRPSDS
Summary
One of main goal in speech recognition is to obtain the best accuracy for recognizing or classifying the speech signal uttered by speaker. The technology of speech recognition is currently used in many applications, such as smart phones, security systems, etc. These systems still have some difficulties in distinguishing syllables or word that sound similar. Many classification methods were applied for classifying the speech signal by previous researchers. Several methods such as the Hidden Markov Model (HMM), Support Vector Machines (SVM), Gaussian Mixture Model (GMM), and Multi-layer Perceptron (MLP) or Artificial Neural Network (ANN) as classifiers [1 - 8]. MLP or ANN is a method when learning algorithm is performed and converged It involves of weights and the ability of the underlying networks to implement desired function using sufficient number of hidden neuron. The number of neurons in hidden layer influenced the network
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