Machine learning for Arabic phonemes recognition using electrolarynx speech

Abstract

<p><span lang="EN-US">Automatic speech recognition system is one of the essential ways of interaction with machines. Interests in speech based intelligent systems have grown in the past few decades. Therefore, there is a need to develop more efficient methods for human speech recognition to ensure the reliability of communication between individuals and machines. This paper is concerned with Arabic phoneme recognition of electrolarynx device. Electrolarynx is a device used by cancer patients having vocal laryngeal cords removed. Speech recognition here is considered to find the preferred machine learning model that can classify phonemes produced by electrolarynx device. The phonemes recognition employs different machine learning schemes, including convolutional neural network, recurrent neural network, artificial neural network (ANN), random forest, extreme gradient boosting (XGBoost), and long short-term memory. Modern standard Arabic is utilized for testing and training phases of the recognition system. The dataset covers both an ordinary speech and electrolarynx device speech recorded by the same person. Mel frequency cepstral coefficients are considered as speech features. The results show that the ANN machine learning method outperformed other methods with an accuracy rate of 75%, a precision value of 77%, and a phoneme error rate (PER) of 21.85%.</span></p>

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“…Furthermore, in [56] multilayer perceptron (MLP) slightly outperformed the XGBoost methodology with 99.3 % precision and was suggested by the authors as the optimal solution. Moreover, regarding language processing, XGBoost was proposed for sentiment features selection [37] and text similarity identification [57] with an F1 score of 69% and 89% respectively, whereas in [58] ANN was selected for human speech recognition with 77% precision. Finally, in terms of environmental applications, [59] combined a grid search algorithm and XGBoost model for hyperparameter fine tuning and electricity load prediction respectively, similarly [60] proved that ensemble techniques provide an efficient solution for solar radiation forecasting.…”