Robust Speech Recognition Using Adaptive Noise Cancellation

Abstract

This paper introduces the adaptive noise cancellation technique for the noise reduction in Robust Automatic Speech Recognition. The adaptive noise cancellation is used as front-end stage to enhance the extracted features for speech recognition under noisy conditions. More specifically, the Constrained Stability Least Mean Square (CS-LMS) algorithm which is a member of the family of adaptive filters has been applied. The Hidden Markov Model based Tool Kit (HTK) is used for training and testing the Automatic Speech Recognizer system. The result obtained shows that the application of adoptive filtering at the front-end enhances the performance of the system in noisy conditions while the CS-LMS algorithm gives the most superior performance among the family of LMS algorithms.