Optimal Adaptive Filtering Technique for Tamil Speech Enhancement

Abstract

Noise reduction in speech applications has a significant amount of research for several decades. The ultimate goal of the speech signal processing research is to develop systems which can perform well in noisy environments. The main objective of this research work is to obtain a clean speech signal of higher quality by applying the optimal noise cancellation technique. In recent years, the real-time adaptive filtering algorithms are considered to be an essential technique for noise cancellation. In this paper, the most popular adaptive filtering algorithms namely Least Mean Square (LMS), Normalized Least Mean Squares (NLMS) and Recursive Least Squares (RLS) algorithms are analyzed and implemented for Tamil speech enhancement. Based on the experimental results, it can be observed that the performance of the adaptive filters is better than using conventional methods designed for speech enhancement. The performances of these algorithms are evaluated based on the metrics namely PSNR, MSE, SNR and SNR Loss. Based on the performance evaluation, the RLS algorithm was found to be a better optimal noise cancellation technique for Tamil speech signals.

Normal and Whispered Speech Recognition Systems for Myanmar Digits

“…Many researcher also use it for the speech recognition research of various languages. According to the studied papers, MFCC and HMM techniques can give higher recognition rate than other methods [6]. In this experiment, we also use MFCC and HMM to recognize Myanmar digits.…”