Open Access

ARTICLE

Implementation of Hybrid Deep Reinforcement Learning Technique for Speech Signal Classification

R. Gayathri^1,*, K. Sheela Sobana Rani²

1 Department of Electronics and Communication Engineering, Sri Ramakrishna Institute of Technology, Coimbatore, 641020, India
2 Department of Electrical and Electronics Engineering, Sri Ramakrishna Institute of Technology, Coimbatore, 641020, India

* Corresponding Author: R. Gayathri. Email:

Computer Systems Science and Engineering 2023, 46(1), 43-56. https://doi.org/10.32604/csse.2023.032491

Received 19 May 2022; Accepted 27 August 2022; Issue published 20 January 2023

Abstract

Classification of speech signals is a vital part of speech signal processing systems. With the advent of speech coding and synthesis, the classification of the speech signal is made accurate and faster. Conventional methods are considered inaccurate due to the uncertainty and diversity of speech signals in the case of real speech signal classification. In this paper, we use efficient speech signal classification using a series of neural network classifiers with reinforcement learning operations. Prior classification of speech signals, the study extracts the essential features from the speech signal using Cepstral Analysis. The features are extracted by converting the speech waveform to a parametric representation to obtain a relatively minimized data rate. Hence to improve the precision of classification, Generative Adversarial Networks are used and it tends to classify the speech signal after the extraction of features from the speech signal using the cepstral coefficient. The classifiers are trained with these features initially and the best classifier is chosen to perform the task of classification on new datasets. The validation of testing sets is evaluated using RL that provides feedback to Classifiers. Finally, at the user interface, the signals are played by decoding the signal after being retrieved from the classifier back based on the input query. The results are evaluated in the form of accuracy, recall, precision, f-measure, and error rate, where generative adversarial network attains an increased accuracy rate than other methods: Multi-Layer Perceptron, Recurrent Neural Networks, Deep belief Networks, and Convolutional Neural Networks.

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Keywords

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