Deep Learning-Based Classification of Spoken English Digits.

Abstract

Classification of isolated digits is the basic challenge for many speech classification systems. While a lot of work has been carried out on spoken languages, only limited research work on spoken English digit data has been reported in the literature. The paper proposes an intelligent-based system based on deep feedforward neural network (DFNN) with hyperparameter optimization techniques, an ensemble method; random forest (RF), and a regression method; gradient boosting (GB) for the classification of spoken digit data. The paper investigates different machine learning (ML) algorithms to determine the best method for the classification of spoken English digit data. The DFNN classifier outperformed the RF and GB classifiers on the public benchmark spoken English digit data and achieved 99.65% validation accuracy. The outcome of the proposed model performs better compared to existing models with only traditional classifiers.