Deep Neural Learning Adaptive Sequential Monte Carlo for Automatic Image and Speech Recognition. (7th October 2020)

• Record Type: Journal Article
• Authors: Kamsing, Patcharin; Torteeka, Peerapong; Boonpook, Wuttichai; Cao, Chunxiang
• Other Names: He Jun Academic Editor.
• Is Part Of: Applied computational intelligence and soft computing. Volume 2020(2020)
• Publication Date: 2020-10-07
• Subjects: Computational intelligence -- Periodicals; Soft computing -- Periodicals; 006.305
• DOI: 10.1155/2020/8866259
• Languages: English
• ISSNs: 1687-9724

Abstract

Abstract : To enhance the performance of image classification and speech recognition, the optimizer is considered an important factor for achieving high accuracy. The state-of-the-art optimizer can perform to serve in applications that may not require very high accuracy, yet the demand for high-precision image classification and speech recognition is increasing. This study implements an adaptive method for applying the particle filter technique with a gradient descent optimizer to improve model learning performance. Using a pretrained model helps reduce the computational time to deploy an image classification model and uses a simple deep convolutional neural network for speech recognition. The applied method results in a higher speech recognition accuracy score—89.693% for the test dataset—than the conventional method, which reaches 89.325%. The applied method also performs well on the image classification task, reaching an accuracy of 89.860% on the test dataset, better than the conventional method, which has an accuracy of 89.644%. Despite a slight difference in accuracy, the applied optimizer performs well in this dataset overall.