Computational Intelligence for Speech Enhancement using Deep Neural Network - D., Hepsiba; Justin, Judith (Computer Assisted Methods in Engineering and Science, 2022)
In real time, the speech signal received contains noise produced in the background and reverberations. These disturbances reduce the quality of speech; therefore, it is important to eliminate the noise and increase the intelligibility and quality of speech signal. Speech enhancement is the primary task in any real-time application that handles speech signals. In the proposed method, the most effective and challenging noise, i.e., babble noise, is removed, and the clean speech is recovered. The enhancement of the corrupted speech signal is done by applying a deep neural network-based denoising algorithm in which the ideal ratio mask is used to mask the noisy speech and separate the clean speech signal. In the proposed system, the speech signal corrupted by noise is enhanced. Evaluation of enhanced speech signal by performance metrics such as short time objective intelligibility and signal to noise ratio of the denoised speech show that the speech intelligibility and speech quality are improved by the proposed method.