Enhancement of Speech Signal Using Improved FA-ANFIS Classifier for Hearing AIDS Application - N. , Shanmugapriya (International Journal of Psychosocial Rehabilitation, 2020)
Research is undergoing in hearing aids application in the sense of dealing with background noise in speech. It aims to understand the speech in the availability of background noise. It acts as challenging environment to enhance the speech understanding during presence of noise. Thus, hearing aid applications had been introduced to understand the speech even in the presence of noise in various environments for end users. It is one type of methodology adopted for improving the user to understand the information in signal. This speech enhancement application helps to improve availability of data. Additionally, it increases the speech signal intelligibility and quality of the application. This review introduced the mechanism for improving these above-mentioned characteristics in applications such as hearing aids. This could be followed out using enhancement methods namely, Fast Independent Component Analysis algorithm shortly Fast ICA. This helps to reduce the noise presence in speech-related signals. This process also involves IDWT techniques, where IDWT stands for Improved Discrete Wavelet Transform, to do feature selection. Features have been extracted from the speech signal that are denoised. AANOVA stands for Advanced Analysis of Variance and helps to identify the important feature from the features that are already selected in the previous process. Finally, the features that are extracted as important have been given for the optimization algorithm in order to get the features in an optimized manner. Now lastly collected features are then classified efficiently using ANFIS. ANFIS stands for Adaptive Neural Fuzzy Inference System, which is a classification approach. It is also called the Improved FAANFIS Method. The result obtained in the proposed approach proves that it would give out promisingly better speech intelligibility and good quality over speech signals in hearing aid commodities as compared to applications that already exist.